diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 82a0e8c5..ffe4e44c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -20,7 +20,6 @@ jobs:
online-feature-store-changed: ${{ steps.changes.outputs.online-feature-store }}
go-sdk-changed: ${{ steps.changes.outputs.go-sdk }}
py-sdk-changed: ${{ steps.changes.outputs.py-sdk }}
- helix-client-changed: ${{ steps.changes.outputs.helix-client }}
inferflow-changed: ${{ steps.changes.outputs.inferflow }}
skye-changed: ${{ steps.changes.outputs.skye }}
multiple-components: ${{ steps.check-multiple.outputs.multiple }}
@@ -48,8 +47,6 @@ jobs:
- 'go-sdk/**'
py-sdk:
- 'py-sdk/**'
- helix-client:
- - 'helix-client/**'
inferflow:
- 'inferflow/**'
skye:
@@ -77,9 +74,6 @@ jobs:
if [ "${{ steps.changes.outputs.py-sdk }}" == "true" ]; then
changed_count=$((changed_count + 1))
fi
- if [ "${{ steps.changes.outputs.helix-client }}" == "true" ]; then
- changed_count=$((changed_count + 1))
- fi
if [ "${{ steps.changes.outputs.inferflow }}" == "true" ]; then
changed_count=$((changed_count + 1))
fi
@@ -96,7 +90,7 @@ jobs:
- name: Check if any component changed
id: check-any
run: |
- if [ "${{ steps.changes.outputs.horizon }}" == "true" ] || [ "${{ steps.changes.outputs.trufflebox-ui }}" == "true" ] || [ "${{ steps.changes.outputs.numerix }}" == "true" ] || [ "${{ steps.changes.outputs.online-feature-store }}" == "true" ] || [ "${{ steps.changes.outputs.go-sdk }}" == "true" ] || [ "${{ steps.changes.outputs.py-sdk }}" == "true" ] || [ "${{ steps.changes.outputs.helix-client }}" == "true" ] || [ "${{ steps.changes.outputs.inferflow }}" == "true" ] || [ "${{ steps.changes.outputs.skye }}" == "true" ]; then
+ if [ "${{ steps.changes.outputs.horizon }}" == "true" ] || [ "${{ steps.changes.outputs.trufflebox-ui }}" == "true" ] || [ "${{ steps.changes.outputs.numerix }}" == "true" ] || [ "${{ steps.changes.outputs.online-feature-store }}" == "true" ] || [ "${{ steps.changes.outputs.go-sdk }}" == "true" ] || [ "${{ steps.changes.outputs.py-sdk }}" == "true" ] || [ "${{ steps.changes.outputs.inferflow }}" == "true" ] || [ "${{ steps.changes.outputs.skye }}" == "true" ]; then
echo "any=true" >> $GITHUB_OUTPUT
else
echo "any=false" >> $GITHUB_OUTPUT
@@ -116,7 +110,6 @@ jobs:
echo "- Online Feature Store CI: ${{ needs.detect-changes.outputs.online-feature-store-changed }}"
echo "- Go SDK CI: ${{ needs.detect-changes.outputs.go-sdk-changed }}"
echo "- Python SDK CI: ${{ needs.detect-changes.outputs.py-sdk-changed }}"
- echo "- Helix Client CI: ${{ needs.detect-changes.outputs.helix-client-changed }}"
echo "- Inferflow CI: ${{ needs.detect-changes.outputs.inferflow-changed }}"
echo "- Skye CI: ${{ needs.detect-changes.outputs.skye-changed }}"
echo "This workflow will proceed with integration tests..."
@@ -137,7 +130,6 @@ jobs:
echo "Testing Numerix matrix operations integration..."
echo "Testing Go SDK compatibility with backend services..."
echo "Testing Python SDK compatibility with backend services..."
- echo "Testing Helix Client compatibility with backend services..."
echo "Testing Inferflow compatibility with backend services..."
# Add actual integration test commands here
@@ -218,6 +210,5 @@ jobs:
echo "- Online Feature Store: ${{ needs.detect-changes.outputs.online-feature-store-changed }}" >> $GITHUB_STEP_SUMMARY
echo "- Go SDK: ${{ needs.detect-changes.outputs.go-sdk-changed }}" >> $GITHUB_STEP_SUMMARY
echo "- Python SDK: ${{ needs.detect-changes.outputs.py-sdk-changed }}" >> $GITHUB_STEP_SUMMARY
- echo "- Helix Client: ${{ needs.detect-changes.outputs.helix-client-changed }}" >> $GITHUB_STEP_SUMMARY
echo "- Inferflow: ${{ needs.detect-changes.outputs.inferflow-changed }}" >> $GITHUB_STEP_SUMMARY
echo "- Skye: ${{ needs.detect-changes.outputs.skye-changed }}" >> $GITHUB_STEP_SUMMARY
\ No newline at end of file
diff --git a/.github/workflows/helix-client.yml b/.github/workflows/helix-client.yml
deleted file mode 100644
index ff236ded..00000000
--- a/.github/workflows/helix-client.yml
+++ /dev/null
@@ -1,71 +0,0 @@
-name: Helix Client CI
-
-on:
- pull_request:
- branches: [main, develop]
- paths: ['helix-client/**']
- push:
- branches: [main, develop]
- paths: ['helix-client/**']
-
-jobs:
- build-helix-client:
- runs-on: ubuntu-latest
- defaults:
- run:
- working-directory: ./helix-client
- steps:
- - name: Checkout code
- uses: actions/checkout@v4
-
- - name: Set up Go
- uses: actions/setup-go@v4
- with:
- go-version: '1.24'
- cache: false
-
- - name: Cache Go modules
- uses: actions/cache@v3
- with:
- path: |
- ~/.cache/go-build
- ~/go/pkg/mod
- key: ${{ runner.os }}-helix-client-${{ hashFiles('helix-client/go.sum') }}
- restore-keys: |
- ${{ runner.os }}-helix-client-
-
- - name: Download dependencies
- run: go mod download
-
- - name: Verify dependencies
- run: go mod verify
-
- - name: Run tests
- run: go test -v ./...
-
- - name: Run tests with coverage
- run: go test -v -coverprofile=coverage.out ./...
-
- - name: Display coverage
- run: go tool cover -func=coverage.out
-
- - name: Build SDK packages
- run: go build -v ./...
-
- - name: Run go vet
- run: go vet ./...
-
- - name: Install staticcheck
- run: go install honnef.co/go/tools/cmd/staticcheck@latest
-
- - name: Run staticcheck
- run: staticcheck ./...
-
- - name: Check for go mod tidy
- run: |
- go mod tidy
- if [ -n "$(git status --porcelain go.mod go.sum)" ]; then
- echo "go.mod or go.sum is not tidy"
- git diff go.mod go.sum
- exit 1
- fi
\ No newline at end of file
diff --git a/.github/workflows/release-helix-client.yml b/.github/workflows/release-helix-client.yml
deleted file mode 100644
index 5ed33890..00000000
--- a/.github/workflows/release-helix-client.yml
+++ /dev/null
@@ -1,151 +0,0 @@
-name: Release Helix Client
-
-on:
- workflow_dispatch:
- inputs:
- version:
- description: 'Version to release (e.g., v0.1.20 or v0.1.20-beta.1)'
- required: true
- type: string
- is_beta:
- description: 'Is this a beta release?'
- required: false
- type: boolean
- default: false
- is_alpha:
- description: 'Is this an alpha release?'
- required: false
- type: boolean
- default: false
- branch:
- description: 'Branch to release from'
- required: true
- type: string
- default: 'main'
-
-jobs:
- release:
- runs-on: ubuntu-latest
- permissions:
- contents: write
- steps:
- - name: Checkout repository
- uses: actions/checkout@v4
- with:
- fetch-depth: 0
- ref: ${{ inputs.branch }}
-
- - name: Set up Go
- uses: actions/setup-go@v4
- with:
- go-version: '1.24'
-
- - name: Validate Helix Client
- run: |
- echo "Current branch: ${{ github.ref }}"
- cd helix-client
- go mod tidy
- go test -v ./...
- go build -v ./...
- go vet ./...
-
- - name: Create Git tag
- run: |
- git config --global user.name "github-actions[bot]"
- git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com"
-
- TAG="helix-client/${{ inputs.version }}"
- git tag -a "$TAG" -m "Helix Client release ${{ inputs.version }}"
- git push origin "$TAG"
-
- - name: Create GitHub Release (Production)
- if: ${{ !inputs.is_beta && !inputs.is_alpha }}
- uses: actions/create-release@v1
- env:
- GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- with:
- tag_name: helix-client/${{ inputs.version }}
- release_name: Helix Client ${{ inputs.version }}
- body: |
- ## Helix Client Release ${{ inputs.version }}
-
- ### Installation
- ```bash
- go get github.com/${{ github.repository }}/helix-client@${{ inputs.version }}
- ```
-
- ### Features
- - Go client libraries for BharatMLStack services
- - Numerix, Predator, and Skye client integrations
- - Type-safe API bindings and gRPC support
-
- ### Usage
- ```go
- import "github.com/${{ github.repository }}/helix-client"
- ```
-
- ### Changes
- Please see the commit history for detailed changes.
-
- ### Validation
- - ✅ Tests passed
- - ✅ Build successful
- - ✅ Go vet clean
- draft: false
- prerelease: false
-
- - name: Create Beta Pre-release
- if: ${{ inputs.is_beta }}
- uses: actions/create-release@v1
- env:
- GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- with:
- tag_name: helix-client/${{ inputs.version }}
- release_name: Helix Client ${{ inputs.version }} (Beta)
- body: |
- ## Helix Client Beta Release ${{ inputs.version }}
-
- ⚠️ **This is a beta release from the develop branch** - Use for testing purposes only.
-
- ### Installation
- ```bash
- go get github.com/${{ github.repository }}/helix-client@${{ inputs.version }}
- ```
-
- ### Changes
- Please see the commit history for detailed changes.
-
- ### Validation
- - ✅ Tests passed
- - ✅ Build successful
- - ✅ Go vet clean
- draft: false
- prerelease: true
-
- - name: Create Alpha Pre-release
- if: ${{ inputs.is_alpha }}
- uses: actions/create-release@v1
- env:
- GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- with:
- tag_name: helix-client/${{ inputs.version }}
- release_name: Helix Client ${{ inputs.version }} (Alpha)
- body: |
- ## Helix Client Alpha Release ${{ inputs.version }}
-
- ⚠️ **This is an alpha release from a feature/fix branch** - Experimental build for development and testing.
-
- ### Installation
- ```bash
- go get github.com/${{ github.repository }}/helix-client@${{ inputs.version }}
- ```
-
- ### Changes
- Please see the commit history for detailed changes.
-
- ### Validation
- - ✅ Tests passed
- - ✅ Build successful
- - ✅ Go vet clean
- draft: false
- prerelease: true
\ No newline at end of file
diff --git a/docs-src/blog/authors.yml b/docs-src/blog/authors.yml
index 2af81d4d..376741b0 100644
--- a/docs-src/blog/authors.yml
+++ b/docs-src/blog/authors.yml
@@ -5,22 +5,22 @@ adarsha:
image_url: https://github.com/a0d00kc.png
aditya:
name: Aditya Kumar
- title: SDE-III @ Meesho
+ title: Lead Software Engineer @ Meesho
url: https://github.com/Adit2607
image_url: https://github.com/Adit2607.png
jigar:
name: Jigar Dave
- title: SDE-IV @ Meesho
+ title: Lead Software Engineer @ Meesho
url: https://github.com/jigarpatel26
image_url: https://github.com/jigarpatel26.png
jaya:
name: Jaya Kumar
- title: MLE-III @ Meesho
+ title: Lead ML Engineer @ Meesho
url: https://github.com/jayakommuru
image_url: https://github.com/jayakommuru.png
bhawani:
name: Bhawani Singh
- title: SDE-IV @ Meesho
+ title: Architect @ Meesho
url: https://github.com/singh-bhawani
image_url: https://github.com/singh-bhawani.png
mohit:
diff --git a/docs-src/blog/bharatmlstack-history/post-five/bms.png b/docs-src/blog/bharatmlstack-history/post-five/bms.png
new file mode 100644
index 00000000..b397fc88
Binary files /dev/null and b/docs-src/blog/bharatmlstack-history/post-five/bms.png differ
diff --git a/docs-src/blog/bharatmlstack-history/post-five/index.md b/docs-src/blog/bharatmlstack-history/post-five/index.md
new file mode 100644
index 00000000..6f488eec
--- /dev/null
+++ b/docs-src/blog/bharatmlstack-history/post-five/index.md
@@ -0,0 +1,121 @@
+---
+slug: post-five
+title: "LLM Inference Optimization Techniques: Engineering Sub-Second Latency at Scale"
+authors: [jaya]
+date: 2025-6-2
+tags: [llm, vllm, tensorrt-llm, mlplatform, meesho, bharatmlstack]
+---
+
+
+## LLM Inference Optimization Techniques: Engineering Sub-Second Latency at Scale
+
+Raw execution of Large Language Models is inherently expensive and memory-intensive. To achieve sub-second latency and high throughput, we implement a multi-layered optimization strategy that targets the entire inference stack—from memory management to kernel execution.
+
+## 1. Advanced Memory Management: Paged & Prefix KV Caching
+
+The most significant bottleneck in LLM inference is not always compute, but memory bandwidth—specifically managing the Key-Value (KV) cache.
+
+### Paged KV caching
+
+Standard caching suffers from fragmentation. We use **Paged KV caching**, which operates similarly to an operating system's virtual memory: the KV cache is divided into non-contiguous blocks. This lets us serve larger batch sizes without running out of memory.
+
+### KV cache quantization
+
+To further maximize available memory, we implement **KV cache quantization** (e.g., FP8). By compressing stored attention keys and values from 16-bit to 8-bit, we nearly double the effective context window capacity of the GPU, allowing longer conversations or larger batches without materially degrading quality.
+
+### Prefix caching (the "voice bot" optimizer)
+
+For use cases like GenAI voice bots where the system prompt (e.g., "You are a helpful assistant...") is static across thousands of requests, we enable **prefix caching**.
+
+- **Impact**: By reusing pre-computed KV states for common prefixes, we achieve a cache hit rate of ~90%. This reduces **Time To First Token (TTFT)** by skipping redundant computation of the system prompt.
+
+## 2. Aggressive Quantization (INT4 AWQ & FP8)
+
+Running models in their native 16-bit precision (BF16) restricts maximum batch size and throughput. We use quantization to shrink model weights without sacrificing accuracy.
+
+### INT4 AWQ (Activation-aware Weight Quantization)
+
+For the Llama 3 family, we use **AWQ** to compress weights to 4 bits. This reduces model size by ~75%, allowing larger models to fit into L4 GPU memory and significantly improving token generation speed.
+
+### FP8 precision
+
+For NVIDIA Hopper (H100) architectures, we are exploring **FP8 quantization**, leveraging native FP8 tensor cores to accelerate matrix multiplications while maintaining a higher dynamic range than integer quantization.
+
+- **Verification**: We validate quantized models by comparing dot-product similarity of embeddings against the FP16 baseline, consistently achieving **>99% similarity**.
+
+## 3. Kernel Fusion & Custom Plugins
+
+To minimize overhead from launching thousands of small GPU operations, we fuse them into monolithic kernels using NVIDIA TensorRT plugins.
+
+- **Flash attention & FMHA**: We enable **Fused Multi-Head Attention (FMHA)** combined with flash attention to reduce memory reads/writes.
+- **GEMM plugins**: We use specialized **GEMM** plugins to accelerate transformer linear layers.
+- **Removing input padding**: Instead of padding short sequences to match the longest, we remove input padding so the GPU processes only valid tokens.
+
+## 4. Inflight (Continuous) Batching
+
+Traditional static batching waits for all requests in a batch to finish before returning results—so one long response delays everyone else.
+
+We implement **inflight batching**: as soon as one request completes, its slot is freed and filled by a new request from the queue. This keeps GPUs saturated and decouples latency of short queries from long ones.
+
+## 5. Parallelism Strategies: Scaling Beyond One GPU
+
+For large models (e.g., 70B+ parameters) that cannot fit into the VRAM of a single GPU, we use parallelism strategies.
+
+- **Tensor parallelism (TP)**: Split weight matrices across multiple GPUs (e.g., 4× L4 or 8× A100). Each GPU computes a shard and outputs are reduced at every layer.
+- **Pipeline parallelism (PP)**: Split model layers across GPUs to pipeline compute (e.g., while one GPU computes later layers for Request A, another starts early layers for Request B).
+
+## 6. Speculative Decoding
+
+To reduce inter-token latency (ITL), we explore **speculative decoding**.
+
+- **Mechanism**: A smaller, faster "draft" model speculatively generates a short token sequence (e.g., 5 tokens).
+- **Verification**: The larger target model verifies those tokens in one parallel forward pass. If correct, we effectively generate multiple tokens per large-model step; if not, we discard and regenerate. This is effective for predictable text, improving perceived generation speed.
+
+## Few Benchmarks
+
+Below are a couple of representative use cases and performance numbers.
+
+### Search query rewriting
+
+- **LLM**: Fine-tuned llama-3.2-1B
+- **Input & output token length**: ~10–20
+- **Response type**: Non-streaming
+
+| Inference runtime | Hardware | Max requests/sec | Max p99 latency |
+| --- | --- | ---: | ---: |
+| TensorRT-LLM | 4 × L4 GPUs (multi-GPU) | 1000 | 95 ms |
+| TensorRT-LLM | 1 × A100 40 GB GPU | 1000 | 69 ms |
+
+### Voice bot query
+
+- **LLM**: Llama-3.1-8B
+- **Input token length**: ~1900–2000
+- **Output token length**: ~200
+- **Response type**: Streaming
+
+| Inference runtime | Concurrency | p99 TTFT (ms) | p99 ITL (ms) | Token throughput (tokens/sec) | Request throughput (req/sec) | Hardware |
+| --- | ---: | ---: | ---: | ---: | ---: | --- |
+| TensorRT-LLM | 1 | 36.27 | 22.78 | 45.66 | 0.23 | L4 |
+| TensorRT-LLM | 2 | 49.81 | 23.21 | 89.37 | 0.45 | L4 |
+| TensorRT-LLM | 4 | 55.33 | 36.62 | 153.39 | 0.78 | L4 |
+| TensorRT-LLM | 8 | 66.5 | 39.11 | 279.88 | 1.47 | L4 |
+| TensorRT-LLM | 16 | 131.8 | 30.39 | 547.8 | 2.77 | L4 |
+| TensorRT-LLM | 32 | 277.22 | 48.02 | 925.7 | 4.78 | L4 |
+| TensorRT-LLM | 64 | 498.52 | 71.62 | 1,164.40 | 6.2 | L4 |
+| TensorRT-LLM | 128 | 677.31 | 120.37 | 1,445.18 | 7.69 | L4 |
+| TensorRT-LLM | 256 | 1,926.31 | 216.88 | 1,600.81 | 8.52 | L4 |
+| TensorRT-LLM | 1 | 21.17 | 9.24 | 130.05 | 0.68 | A100 |
+| TensorRT-LLM | 2 | 25.78 | 9.21 | 264.5 | 1.35 | A100 |
+| TensorRT-LLM | 4 | 28.52 | 10.99 | 437.69 | 2.27 | A100 |
+| TensorRT-LLM | 8 | 34.4 | 12.61 | 760.49 | 3.96 | A100 |
+| TensorRT-LLM | 16 | 68.03 | 14.32 | 1,343.80 | 7.01 | A100 |
+| TensorRT-LLM | 32 | 185.96 | 16.82 | 2,287.30 | 11.92 | A100 |
+| TensorRT-LLM | 64 | 136.87 | 21.17 | 3,625.22 | 18.89 | A100 |
+| TensorRT-LLM | 128 | 463.78 | 34.15 | 4,456.51 | 23.24 | A100 |
+| TensorRT-LLM | 256 | 890.12 | 59.18 | 5,188.24 | 27.05 | A100 |
+
+## Conclusion
+
+High-performance LLM inference is fundamentally a systems engineering problem: memory efficiency, kernel execution, batching strategy, and parallelism determine real-world latency and throughput. Techniques such as paged KV caching, aggressive quantization, kernel fusion, and inflight batching improve GPU utilization while reducing latency and memory pressure.
+
+These optimizations enable the platform to deliver sub-second responses, sustain high concurrency, and efficiently serve both lightweight and long-context workloads. By continuously optimizing across the full inference stack, we keep LLM serving scalable, cost-efficient, and production-ready for real-time AI applications.
diff --git a/docs-src/blog/bharatmlstack-history/post-four/bms.png b/docs-src/blog/bharatmlstack-history/post-four/bms.png
new file mode 100644
index 00000000..b397fc88
Binary files /dev/null and b/docs-src/blog/bharatmlstack-history/post-four/bms.png differ
diff --git a/docs-src/blog/bharatmlstack-history/post-four/index.md b/docs-src/blog/bharatmlstack-history/post-four/index.md
new file mode 100644
index 00000000..23d97525
--- /dev/null
+++ b/docs-src/blog/bharatmlstack-history/post-four/index.md
@@ -0,0 +1,204 @@
+---
+slug: post-four
+title: "Designing a Production-Grade LLM Inference Platform: From Model Weights to Scalable GPU Serving"
+authors: [jaya]
+date: 2025-3-29
+tags: [llm, vllm, tensorrt-llm, mlplatform, meesho, bharatmlstack]
+---
+
+
+## Designing a Production-Grade LLM Inference Platform: From Model Weights to Scalable GPU Serving
+
+
+
+Serving large language models in production introduces new challenges across infrastructure, performance optimization, and operational lifecycle management. The LLM Inference Platform addresses these challenges by providing a unified system for deploying and managing open-source and fine-tuned LLMs at scale.
+
+The platform implements a complete LLMOps lifecycle — from model registration and automated compilation to deployment, runtime optimization, and monitoring. Designed as a self-service environment, users can onboard models directly from open repositories such as Hugging Face or upload custom fine-tuned models, and deploy them using a single-click workflow with no manual infrastructure or configuration steps required.
+
+In addition to fully automated deployment, the platform allows users to select and apply custom inference optimization techniques — such as quantization strategies, batching configurations, and runtime-specific performance enhancements — enabling teams to balance latency, throughput, and cost based on their use case. The goal is to reduce operational friction while enabling high-performance, production-grade LLM inference.
+
+## Why LLM Inference Is not just bigger ML model serving
+
+Large language model (LLM) inference introduces a fundamentally different set of challenges compared to traditional machine learning inference. While classical ML models typically perform a single forward pass to produce a fixed prediction, LLMs operate as autoregressive systems, generating outputs token by token based on previously generated context. This difference dramatically changes how inference systems must be designed, optimized, and scaled.
+
+### Autoregressive Generation and Sequential Computation:
+
+Unlike traditional models such as classifiers or recommenders — where inference cost is relatively constant — LLMs generate responses incrementally. Each new token depends on all previously generated tokens, making inference inherently sequential and dynamic. This means latency and compute requirements vary significantly depending on prompt length and output size, introducing complexity in scheduling and resource allocation.
+Because tokens cannot be generated fully in parallel during decoding, GPUs may become underutilized without specialized batching and scheduling strategies. This has led to the development of dedicated LLM inference engines optimized for token-level execution.
+
+### Prefill and Decode Phases:
+
+LLM inference typically consists of two distinct stages:
+
+- Prefill phase — the model processes the input prompt and builds internal representations. This stage is compute-heavy and highly parallelizable.
+- Decode phase — the model generates tokens sequentially, predicting one token at a time using previously generated context.
+
+The decode stage often becomes memory-bound rather than compute-bound, which creates new performance bottlenecks compared to traditional ML workloads.
+
+### Context Management and KV Caching:
+
+Another fundamental difference lies in how LLMs maintain context. Transformer-based models rely on attention mechanisms that require access to past token representations. To avoid recomputing these representations repeatedly, inference engines use key-value (KV) caching, which stores intermediate activations from previous tokens.
+KV caching significantly improves performance by eliminating redundant computation, but it introduces new challenges:
+
+- Memory consumption grows with sequence length and batch size
+- GPU memory becomes a critical bottleneck
+- Efficient memory management becomes essential for scaling concurrent requests
+
+This tradeoff between compute efficiency and memory usage is unique to LLM inference workloads.
+
+### Dynamic and Irregular Workloads:
+
+Traditional ML inference typically operates on fixed-size inputs with predictable latency. In contrast, LLM requests vary widely in prompt length, output length, and runtime behavior. As a result:
+
+- Batch sizes must be dynamic rather than static
+- Requests may enter and leave batches asynchronously
+- Scheduling systems must continuously rebalance workloads to maximize GPU utilization
+
+These characteristics require specialized serving architectures that differ significantly from standard ML serving pipelines.
+
+### Streaming and User Experience Constraints:
+
+Another distinguishing factor is the expectation of real-time streaming responses. Instead of returning a single output, LLM systems often stream tokens to users as they are generated.
+Because of these differences — sequential generation, growing memory requirements, dynamic workloads, and streaming constraints — LLM inference cannot be treated as a simple extension of existing ML serving systems. Production platforms must incorporate specialized runtime engines, advanced optimization techniques, and observability tailored specifically to LLM workloads.
+
+## LLMOps: High-Level Architecture
+
+
+
+The LLM Inference Framework is designed as a fully automated, end-to-end system for deploying and operating open-source and fine-tuned large language models at scale. The architecture abstracts the complexity of model optimization, hardware selection, deployment, and runtime management into a unified workflow that enables users to move from raw model weights to production-ready inference endpoints with minimal manual intervention.
+
+Our LLM Inference Framework is architected not just as a serving engine, but as a complete lifecycle management system. As illustrated in the high-level design below, the platform automates the journey of a model through seven distinct stages, ensuring reproducibility, performance, and scalability.
+
+1. Onboarding & Registration (The Source of Truth)
+
+ The lifecycle begins with the Data Scientist or engineer.
+
+ - Model Ingestion: Users onboard models—whether open-source (Hugging Face, NeMo) or internally fine-tuned—via the Truffle Box SDK/UI.
+ - LLM + Prompt Registry: Unlike traditional systems that only track model weights, our registry is a unified control plane. It stores both the Model Artifacts and the Prompt Templates. This allows Data Scientists to register and version-control prompts (e.g., "customer_support_v2") independently of the application code.
+
+2. The "Black Box" Build Engine
+
+ Once a model is registered, the Automated LLM Compiler + Quantizer Module kicks off a background job on ephemeral GPU resources.
+
+ - Transformation: The raw model is converted into a TRT-LLM Checkpoint.
+ - Quantization: The system automatically applies quantization algorithms (like INT4 AWQ or FP8) to reduce memory footprint.
+ - Engine Building: Finally, it compiles a highly optimized TRT Engine specifically tuned for the target hardware.
+
+3. Intelligent Profiling & Validation
+
+ Before deployment, the new engine passes through the Hardware & Inference Runtime Profiler.
+
+ - Benchmarking: This module empirically tests the engine against various hardware configurations (L4 vs. A100) and runtimes (TRT-LLM vs. vLLM).
+ - Optimization: It recommends the optimal configuration that meets latency SLAs (Time-To-First-Token) while minimizing cost.
+
+4. Smart Artifact Generation & Distribution
+
+ To solve the Kubernetes "Cold Start" problem, the LLM Serving Artifacts Generation module packages the model using a bifurcated strategy:
+
+ - Standard Models: Artifacts are uploaded to Cloud Storage (GCS) and downloaded by pods at startup.
+ - Very Large Models: For massive models (>8GB) where network downloads are too slow, the system pre-caches the model onto Secondary Boot Disks. These disks are attached directly to new GPU nodes during autoscaling, eliminating download wait times.
+
+5. Image Streaming & Deployment
+
+ Simultaneously, the inference runtime container images are pulled from the Artifact Registry.
+
+ - Image Streaming: We utilize container image streaming to allow pods to start initializing while the massive Triton/Dynamo container layers are still downloading, further shaving seconds off the startup time. link
+
+6. The Inference Runtime (Kubernetes)
+
+ The workload lands on Kubernetes with Autoscaling.
+
+ - Dynamic Backends: Depending on the profile generated in Stage 3, the pod initializes either TensorRT-LLM (for throughput) or vLLM (for flexibility), or spins up a Dynamo worker for distributed inference.
+ - Data Loading: The pod either downloads the model from Cloud Storage or mounts the pre-warmed Secondary Boot Disk ("Pull from Disk").
+
+7. Client Interaction & Observability
+
+ Finally, the LLM Inference Client executes the request.
+
+ - Prompt Injection: The client pulls the specific prompt template ID from the Registry, ensuring the exact versioned instructions are used.
+ - Streaming Response: The request is sent via gRPC, and tokens are streamed back to the user in real-time.
+
+8. Observability: Monitoring the Pulse of GenAI
+
+ In traditional microservices, success is measured by CPU utilization and request latency (p99). For Large Language Models, these metrics are insufficient. A user doesn't care if the GPU is at 80% utilization; they care about how fast the first word appears and how smoothly the rest of the sentence follows.
+
+ To capture the true user experience, our platform instrumentation focuses on three critical LLM-specific metrics:
+
+ 1. Time to First Token (TTFT)
+ - Definition: TTFT measures the time elapsed from the moment a request is received until the very first token is generated and streamed back to the user.
+ - Why it matters: This represents the "Prefill Phase" latency—the time the model takes to process the input prompt and load weights. A high TTFT makes the application feel unresponsive or "hung."
+ - Optimization: We closely monitor TTFT to ensure our Prefix Caching is effective (aiming for high cache hitrates), which drastically lowers this metric by skipping redundant prompt processing.
+
+ 2. Inter-Token Latency (ITL)
+ - Definition: ITL measures the average time interval between the generation of consecutive tokens during the "Decode Phase".
+ - Why it matters: This defines the "perceived speed" of reading. Even if the first token is fast (low TTFT), high ITL makes the text generation look "jerky" or slow to the user.
+ - Benchmarks: In our testing with Llama 3.1, we track p99 ITL to ensure it stays below human reading speeds to maintain a natural conversational flow.
+
+ 3. Token Throughput vs. Request Throughput
+ - We distinguish between two types of throughput to balance system efficiency with user load:
+ - Token Throughput (tokens/sec): The total number of tokens generated across all concurrent requests. This measures the raw compute efficiency of the GPU and the effectiveness of batching.
+ - Request Throughput (req/sec): The number of distinct user queries served per second. We use this to determine autoscaling thresholds, ensuring we scale out before the queue depth impacts ITL.
+
+ 4. The Monitoring Stack
+ - Real-time Dashboards: We utilize Grafana to visualize these streaming metrics in real-time, allowing on-call engineers to spot "slow generation" incidents that generic "500 error" alerts would miss.
+ - Request Tracing: Since Triton Inference Server does not log request payloads by default, we integrate a Helix Client to asynchronously publish request logs to Log Tables. This allows us to trace a specific "slow" request back to its prompt to understand if a complex input caused the latency spike.
+
+## Supported Inference backends (TensorRT LLM, Dynamo & vLLM)
+
+Tailored for the Use Case: We do not believe in a "one-size-fits-all" approach to inference. Different use cases—whether a real-time voice bot requiring ultra-lowsub-second latency or a massive reasoning task requiring huge context windows—demand different runtime characteristics. Our platform is designed to be runtime-agnostic, allowing us to automatically select and tailor the best engine based on the specific requirements of the application:
+
+1. TensorRT-LLM: The High-Performance Standard
+
+ Suitable for: High-throughput production workloads where latency is critical (e.g., customer support chat, real-time voice bots).
+
+ TensorRT-LLM serves as our default backend for these scenarios. Our internal benchmarks on Llama 3.1 and 3.2 models demonstrated that a tuned TensorRT-LLM engine significantly outperforms standard runtimes, especially when utilizing INT4 AWQ and FP8 quantization .
+
+ Key optimizations we tailor for these high-load cases include:
+
+ - Optimized execution via TensorRT engine compilation
+ - Quantization-aware execution for reduced memory usage and improved throughput
+ - Inflight Batching: Allowing requests to be processed continuously without waiting for the entire batch to finish, drastically improving GPU utilization .
+ - Custom Plugins: Enabling specific NVIDIA plugins like the GEMM plugin and GPT Attention plugin to accelerate matrix multiplications and attention mechanisms .
+
+2. Dynamo: Distributed Inference for Reasoning Models
+
+ Suitable for: Very large "reasoning" models (70B+) or scenarios requiring massive context windows where a single GPU's memory is insufficient.
+
+ For these memory-bound tasks, we utilize Dynamo, a low-latency distributed inference framework . Unlike monolithic servers, Dynamo disaggregates the inference process to scale resources horizontally:
+
+ - KV Aware Routing: A specialized router directs requests to workers that already hold the relevant Key-Value (KV) cache, minimizing redundant computation .
+ - Prefill vs. Decode Split: The workload is divided into Prefill Workers (processing the prompt) and Decode Workers (generating tokens), allowing us to scale the compute-heavy "reading" phase independently from the memory-heavy "writing" phase .
+ - Distributed execution across multiple GPU resources
+
+3. vLLM: The Flexible Baseline
+
+ Suitable for: Rapid prototyping, testing new model architectures, or low-traffic internal tools where ease of deployment outweighs raw throughput.
+
+ While TensorRT-LLM is optimized for maximum speed, vLLM provides a robust and flexible baseline .
+
+ - High throughput through dynamic batching and efficient memory utilization
+ - Paged KV cache management for handling long contexts and concurrent requests
+ - Strong support for open-source model ecosystems
+ - Rapid Adoption: It allows us to onboard new model architectures immediately without waiting for a custom TensorRT build.
+ - Benchmarking Insight: In our internal tests, vLLM provided a strong baseline but often lacked the specific max-token optimizations present in our custom TRT engines . We use it strategically for initial testing before committing to a full TensorRT optimization pipeline.
+
+## Conclusion
+
+Large language model inference introduces a fundamentally new class of infrastructure challenges—where performance is governed not just by raw compute, but by memory efficiency, intelligent scheduling, runtime specialization, and lifecycle automation. Unlike traditional ML serving, LLM inference requires systems that understand token-level execution, manage rapidly growing context state, and continuously balance latency, throughput, and cost under highly dynamic workloads.
+
+The LLM Inference Framework addresses these challenges by transforming inference into a fully automated, reproducible lifecycle—from model onboarding and compilation to deployment, optimization, and observability. By integrating automated quantization and engine compilation, intelligent runtime selection, cold-start mitigation strategies, and LLM-specific observability metrics such as Time-to-First-Token and Inter-Token Latency, the platform ensures both high performance and operational simplicity.
+
+Equally important, the framework is designed with flexibility and future evolution in mind. Its runtime-agnostic architecture enables seamless adoption of emerging inference engines, hardware accelerators, and optimization techniques without requiring platform redesign. This ensures that teams can continuously leverage advancements in the rapidly evolving LLM ecosystem while maintaining consistent operational workflows.
+
+Ultimately, the goal of the platform is to make production-scale LLM deployment as seamless and reliable as traditional software deployment—allowing teams to focus on building intelligent applications rather than managing infrastructure complexity. By combining lifecycle automation, runtime optimization, and deep observability, the LLM Inference Framework provides a scalable foundation for delivering fast, cost-efficient, and production-ready LLM experiences.
+
+## Future Explorations
+
+While we have achieved significant milestones in latency and throughput, the landscape of GenAI is evolving rapidly. Our roadmap focuses on increasing flexibility, reducing costs, and enhancing reliability for enterprise-grade workloads. Here is what we are building next:
+
+- TPU Support: To diversify our hardware supply chain and further optimize cost-per-token, we are evaluating Google Cloud TPUs to bake it into our platform. By leveraging the JAX and PyTorch/XLA ecosystems, we aim to unlock the massive throughput potential of TPU v5e chips, particularly for our open-source Llama models. This will allow the hardware profiler to dynamically choose between NVIDIA GPUs and Google TPUs based on real-time availability and price-performance metrics.
+- Multi-LoRA Serving (Serverless Experience): Currently, deploying a fine-tuned model requires a dedicated GPU. We are building support for Multi-LoRA serving, which will allow us to serve hundreds of unique, fine-tuned adapters on top of a single frozen base model. This will drastically reduce costs for multi-tenant applications, enabling a "serverless" experience where specific fine-tunes are hot-swapped instantly per request.
+- Spot Instance Orchestration: To further optimize cloud costs, we are developing fault-tolerant mechanisms to run inference workloads on Spot Instances. By implementing aggressive checkpointing and seamless request draining, we aim to leverage cheaper, preemptible compute capacity without interrupting the user's streaming experience.
+- Semantic Caching Layer: We plan to move beyond standard Prefix Caching to implement Semantic Caching. By using a vector database to fetch responses for semantically similar queries (e.g., "How do I reset my password?" vs. "Password reset steps"), we can bypass the GPU entirely for repetitive queries, reducing latency to near-zero.
+- Context-Aware Autoscaling: Standard CPU/GPU utilization metrics are often insufficient signals for scaling LLMs. We are working on KV-cache pressure metrics for autoscaling. This ensures that we scale out before the memory fills up, preventing eviction-based slowdowns during traffic spikes.
+- Online Evaluation & Guardrails: We are integrating a lightweight "Trust Layer" into the proxy. This will allow for low-latency input/output filtering (Guardrails) and asynchronous "LLM-as-a-Judge" evaluation pipelines to monitor response quality in production, not just system health.
\ No newline at end of file
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@@ -6,7 +6,7 @@ date: 2022-11-15
tags: [online-feature-store, interaction-store, mlplatform, meesho]
---
-
+
## The Genesis: How a Friday Night Roast Sparked Meesho’s ML Platform
It all started in early 2022, over a casual Friday evening catch-up. Like many great origin stories, this one began with friendly banter between a group of backend engineers and data scientists. As the conversations unfolded, so did the roasting—until one remark hit a little too close to home:
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+---
+slug: post-three
+title: "Cracking the Code: Scaling Model Inference & Real-Time Embedding Search"
+authors: [aditya, jaya, adarsha]
+date: 2024-05-21
+tags: [model-inference, embedding-search, mlplatform, meesho, bharatmlstack]
+---
+
+
+
+## Cracking the Code: Scaling Model Inference & Real-Time Embedding Search
+
+By mid-2023, we had transformed our ML stack—building a real-time feature store, optimizing model retrieval, and fine-tuning ranking. But two critical gaps remained:
+
+- 🔹 Scaling model inference without hitting infrastructure roadblocks
+- 🔹 Moving embedding search from batch to real-time for candidate generation
+
+Here’s how we tackled these last-mile challenges, broke free from infrastructure constraints, and built a cost-efficient, high-performance system.
+
+## Breaking Free from the Scalability Ceiling
+
+### The Model Serving Bottleneck—A Wake-Up Call
+
+July 2023. With just months left for the Mega Blockbuster Sale (MBS), we noticed a serious issue—scaling our model-serving infrastructure was taking 10–15 minutes. In real-time ML, that’s an eternity.
+In one of our war rooms, we ran a quick experiment:
+
+- 🚀 We deployed an XGBoost model on a self-hosted Triton Inference Server running on a 16-core machine.
+- 🚀 Fired requests and compared the outputs with our existing cloud-hosted setup.
+- 🚀 The results matched—perfectly.
+
+That moment changed everything. We prepped a backup Triton setup on EKS, just in case our cloud provider couldn't allocate enough compute resources in time. Luckily, they did—but the seed was planted.
+Then in October, just two weeks before MBS, we got an alarming response from our infrastructure team:
+ "Node availability may be an issue."
+With no time to waste, we moved 30% of real-time ML traffic to our self-hosted Triton cluster. The results?
+
+- ✅ p99 latency dropped from 90–100ms to 30–40ms
+- ✅ Triton handled significantly higher throughput on fewer resources
+- ✅ No model changes were needed
+
+MBS ran without a hitch, proving that self-hosted inference was the way forward.
+
+### Scaling Triton on GKE
+
+This left us with two choices:
+
+- 1️⃣ Port models to a managed cloud inference service, investing time in learning a new deployment stack
+- 2️⃣ Scale our existing Triton setup on GKE, optimizing for cost and performance
+
+We went with Option 2—and it slashed inference costs to 35% of what we previously paid, while giving us full control over scaling and optimizations.
+
+### Fixing the Cold Start Problem
+
+As we onboarded more deep learning (DL) models, we hit a new bottleneck, new inference pods took 7–9 minutes to spin up.
+
+After profiling, we found the culprits:
+
+- Triton’s base image—a massive 5GB
+- Model binaries—often 1GB+
+- Startup delay—mostly due to downloading and initializing these assets
+
+To fix this, we built a lightweight Triton image, stripping unused components and shrinking the size to 900MB. This cut cold start times drastically, making auto-scaling faster and smoother.
+
+## Embedding Search: The Last Piece of the Puzzle
+
+By mid-2023, most of our ML stack had gone real-time—except for Candidate Generation (CG), which still ran in batch mode. To truly power real-time recommendations, we needed an online embedding search system.
+
+### Choosing the Right Vector Database
+
+We benchmarked three production-ready vector DBs across key parameters:
+
+- Milvus
+- Qdrant
+- Weaviate
+
+After extensive POCs, Qdrant stood out for its:
+
+- ✅ Blazing-fast search latency on high-dimensional vectors
+- ✅ Efficient memory usage, crucial for in-memory workloads
+- ✅ Support for upserts and soft deletes, vital for Ads use cases
+- ✅ gRPC + REST APIs, making integration seamless
+- ✅ Powerful filtering, allowing fine-tuned retrieval (e.g., filtering Ads by category, active status, etc.)
+
+At its core, Qdrant uses HNSW indexing, delivering both high recall and low-latency nearest-neighbor search—a perfect fit for our needs.
+
+### Embedding Freshness & Real-Time Updates
+
+To ensure embeddings stayed up to date, we built a dual ingestion pipeline:
+
+- 📌 Daily Refresh: A bulk pipeline updated embeddings overnight
+- 📌 Real-Time Updates: Ads events triggered immediate upserts/deletes
+
+This setup powered real-time "Similar Products" recommendations on the product page and became the foundation for Ads Candidate Generation, ensuring the right ads surfaced in milliseconds.
+
+
+
+## Final Takeaways: Scaling Smartly for Real-Time ML
+
+- 🚀 Self-hosted inference on Triton gave us lower cost, faster scaling, and better performance than managed services
+- 🚀 Building a custom Triton image reduced cold starts, improving responsiveness
+- 🚀 Qdrant-based embedding search enabled real-time personalization at scale
+- 🚀 Real-time updates for embeddings unlocked dynamic, up-to-date recommendations
+
+By early 2024, Meesho’s ML stack had evolved into a fully real-time, scalable, and cost-efficient system, setting the foundation for even bigger leaps ahead.
+
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+---
+slug: post-two
+title: "Building Meesho’s ML Platform: Lessons from the First-Gen System (Part 2)"
+authors: [bhawani, jigar, adarsha]
+date: 2023-4-10
+tags: [inferflow, interaction-store, mlplatform, meesho, bharatmlstack]
+---
+
+
+## Building Meesho’s ML Platform: Lessons from the First-Gen System (Part 2)
+
+By late 2022, we had built something we were truly proud of—a real-time ML serving system with a DAG-based executor, a feature store, and an interaction store powering key ranking and personalization models. It was a major milestone, the culmination of months of effort from data scientists, ML engineers, and backend teams. Our system was live, and we were ready to push the boundaries of experimentation.
+And it worked. Mostly.
+But soon, cracks appeared. Every new model needed custom feature retrieval logic, DAGs became dense and unmanageable, and scaling turned into a constant firefight. Costs surged, and infra bottlenecks slowed experimentation. Our system worked, but it wasn’t built for scale.
+This is the story of how we tackled these challenges—building Inferflow for seamless feature retrieval, optimizing real-time infra, and cutting costs while scaling to millions of QPS.
+
+### The Cost of Success
+Every new Ranker model required its own feature set, often pulling from different entities. Each addition meant:
+
+- Adding new DAG nodes in IOP
+- Writing custom logic to fetch features from multiple sources (e.g., user, product, user × category)
+- Inferring intermediate features (e.g., extracting category from a product to fetch user × category data)
+- Optimizing I/O and dealing with the inevitable bugs
+
+What began as clean DAGs soon turned into a tangled web of cross-dependent graphs. Every experimentation cycle meant new nodes, new dependencies, and slower iterations.
+
+### Scaling Pains (and Cassandra’s Limits)
+At some point, we were hitting:
+
+- 250–300K reads/sec
+- 1M writes/sec (during lean hours)
+
+All of this ran on Cassandra. While its distributed architecture had been proven in production, operating large-scale clusters came with considerable infrastructure overhead. Our proof-of-concept (POC) demonstrated throughput of around 100K ops/sec, but as we scaled further, the challenges grew. Ensuring node health, optimizing compaction, and maintaining storage balance became increasingly demanding. We also observed latency spikes under heavy load, alongside a sharp increase in total cost of ownership.
+
+### Interaction Store Woes
+Our interaction store was another ticking time bomb:
+
+- 🚨 Clusters kept growing in size and cost
+- 🚨 Latency spikes became increasingly frequent
+- 🚨 The DMC proxy occasionally lost locality of nodes against shards, causing cross-node communication and degraded performance
+
+Each time this happened, we had to manually rebalance shards just to restore stable latency, making operations unsustainable at scale.
+
+### Silver Linings
+Despite the chaos, the system was live and delivering value:
+
+- Real-time infrastructure was in production
+- Costs dropped by 60–70% compared to offline personalization
+- New experiments rolled out faster and more successfully
+- User engagement metrics improved
+
+It wasn’t perfect. It was far from easy. But it worked—and that counted for a lot.
+
+### Round Two: Solving the Top 2 Bottlenecks
+With the first-gen system stretched to its limits, we stepped back. Conversations with data scientists and backend engineers revealed three recurring pain points:
+
+1. Coding feature retrieval logic for every new model was becoming unsustainable
+2. ML scale was exploding—bringing rising infra costs with it
+3. Real-time embedding search was the next big unlock
+
+We tackled them one by one—starting with the biggest pain point.
+
+#### Problem 1: No-Code Feature Retrieval for Model Inference
+We noticed a pattern: for personalized ranking, models needed features from:
+
+- ✅ Product
+- ✅ User
+- ✅ User × Category
+- ✅ Region, cohort, sub-category, etc.
+
+A key insight emerged: Entities that contribute features for a model always map back to the context entities.
+
+
+
+With this, we designed Inferflow, a graph-driven feature retrieval and model orchestration system:
+
+- 1️⃣ Inferflow takes a modelId and context IDs (e.g., userId, productIds)
+- 2️⃣ Loads a pre-defined feature retrieval graph from ZooKeeper
+- 3️⃣ Executes the graph to resolve entity relationships dynamically
+- 4️⃣ Outputs a 2D matrix of feature vectors
+
+💡 The impact?
+
+- 🚀 No more custom feature retrieval code—just graph updates in config
+- 🚀 Feature consistency across experiments
+- 🚀 Faster iteration cycles for ranking, fraud detection, and beyond
+
+Here’s a visual example that shows how this graph plays out during execution. We further extended the graph to call multiple models as needed:
+
+We built Inferflow in GoLang, using gRPC and Proto3 serialization for efficiency.
+
+#### Problem 2: Scaling Without Breaking the Bank
+With more ML use cases coming online, we needed to cut costs without compromising performance. We focused on:
+
+- 🔹 Online Feature Store
+- 🔹 Interaction Store
+
+#### Optimizing the Online Feature Store
+Our costs were concentrated in:
+
+- 📌 Database (Cassandra)
+- 📌 Cache (Redis)
+- 📌 Running Pods (Java services)
+
+1️⃣ Replacing Cassandra with ScyllaDB
+As we hit the operational limits of large Cassandra clusters, we transitioned to ScyllaDB, which offered a seamless drop-in replacement without major code changes. The switch brought significant benefits:
+
+- Throughput: Matched or exceeded Cassandra's performance under identical workloads, even under high concurrency.
+- Latency: Achieved consistently lower P99 latencies due to ScyllaDB's shard-per-core architecture and better I/O utilization.
+- Cost Efficiency: Reduced infra footprint by ~70% through better CPU and memory efficiency, eliminating the need for over-provisioned nodes.
+
+2️⃣ Finding the Right Cache
+To reduce backend load and improve response times, we benchmarked multiple caching solutions—Memcached, KeyDB, and Dragonfly—under real production traffic patterns. Dragonfly stood out due to its robust architecture and operational simplicity:
+
+- Data Skew Handling: Efficiently managed extreme key hotness and uneven access patterns without performance degradation.
+- Throughput: Delivered consistently high throughput, even with large object sizes and concurrent access.
+- Ease of Adoption: Acted as a drop-in Redis replacement with full protocol compatibility—no changes needed in application code or client libraries.
+
+3️⃣ Moving to GoLang for Cost-Efficient Serving
+Java services were memory-heavy—so we rewrote core services in GoLang. The results?
+
+✅ Memory usage dropped by ~80%
+✅ CPU utilization was significantly lower
+✅ Faster, more efficient deployments
+
+#### Optimizing the Interaction Store
+We realized that we only need a user’s interaction data in Redis when they open the app. So, we implemented a tiered storage approach:
+
+- 📌 Cold Tier (ScyllaDB)—Stores click, order, wishlist events
+- 📌 Hot Tier (Redis)—Loads a user’s past interactions only when they open the app
+
+Smart Offloading: We introduced an inactivity tracker to detect when a user session ends. At that point, Redis data was flushed back to Scylla, reducing unnecessary writes.
+
+
+#### Results
+
+- Online Feature Store hit 1M QPS for the first time during the 2023 Mega Blockbuster Sale—without breaking a sweat
+- Infra costs for Online Feature Store and Interaction Store dropped by ~60%
+
+#### The Catch: Our ML Hosting Hit a Hard Limit
+While planning for 2023 MBS, we ran into a critical scalability bottleneck:
+
+- ❌ Insufficient compute availability in our region for ML instances
+- ❌ Couldn’t provision enough nodes to handle real-time inference at scale
+
+This forced us to rethink where and how we hosted our models. The existing setup was great for prototyping—but it wasn’t built to handle the bursty, high-QPS demands of real-world production workloads.
+
+### Conclusion: From Firefighting to Future-Proofing
+What started as an ambitious experiment turned into a real-time ML infrastructure that powered millions of requests per second. We battled scaling pains, rethought feature retrieval with Inferflow, and rebuilt our infra stack for efficiency—driving down costs while improving experimentation velocity.
+But new challenges emerged. Our infrastructure could now handle scale, but our ML model hosting setup hit a hard limit. With compute availability bottlenecks threatening real-time inference, we faced a critical decision: how do we make model serving as scalable and cost-efficient as the rest of our stack? That’s the next piece of the puzzle—and the story of Part 3.
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+{
+ "label": "Inferflow",
+ "position": 2,
+ "link": {
+ "type": "generated-index",
+ "description": "Inferflow is a graph-driven feature retrieval and model inference orchestration engine. It dynamically resolves entity relationships via configurable DAGs, retrieves features from the Online Feature Store, and orchestrates model scoring — all without custom code."
+ }
+}
diff --git a/docs-src/docs/inferflow/v1.0.0/_category_.json b/docs-src/docs/inferflow/v1.0.0/_category_.json
new file mode 100644
index 00000000..0641455e
--- /dev/null
+++ b/docs-src/docs/inferflow/v1.0.0/_category_.json
@@ -0,0 +1,9 @@
+{
+ "label": "v1.0.0",
+ "position": 1,
+ "link": {
+ "type": "generated-index",
+ "description": "Inferflow v1.0.0",
+ "slug": "/inferflow/v1.0.0"
+ }
+}
diff --git a/docs-src/docs/inferflow/v1.0.0/architecture.md b/docs-src/docs/inferflow/v1.0.0/architecture.md
new file mode 100644
index 00000000..789e9a4d
--- /dev/null
+++ b/docs-src/docs/inferflow/v1.0.0/architecture.md
@@ -0,0 +1,267 @@
+---
+title: Architecture
+sidebar_position: 1
+---
+
+# BharatMLStack - Inferflow
+
+Inferflow is part of **BharatMLStack**, a graph-driven feature retrieval and model inference orchestration engine built in **Go**. It eliminates the need for custom feature retrieval code by using configurable DAG topologies to dynamically resolve entity relationships, fetch features from the Online Feature Store, and orchestrate model scoring — all driven by configuration stored in **etcd**.
+
+---
+
+## Overview
+
+In a typical ML serving pipeline, every new model requires bespoke code to:
+- Fetch features from multiple entities (user, product, user x category, etc.)
+- Infer intermediate entity relationships (e.g., extract category from product to fetch user x category data)
+- Orchestrate one or more model inference calls
+- Handle I/O, batching, and error propagation
+
+Inferflow abstracts all of this behind a **config-driven DAG executor**. Given a `model_config_id` and context entities (e.g., `userId`, `productIds`), it:
+
+1. Loads a pre-defined feature retrieval and inference graph from etcd
+2. Executes the graph to resolve entity relationships dynamically
+3. Retrieves features from the Online Feature Store (OnFS) in parallel
+4. Calls model serving endpoints (Predator) and compute services (Numerix)
+5. Returns scored results as a structured response
+
+---
+
+## High-Level Architecture
+
+
+
+The diagram shows the internal DAG structure of Inferflow's topology executor. gRPC APIs (Pair, Point, Slate) feed into the DAG, where **Feature Init** bootstraps the ComponentMatrix. Feature components (FS User, FS Product, FS Region, FS User Cat, FS Region Scat) fetch features from **OnFS** in parallel and populate columns in the shared **2D Result Matrix**. Model components (Model A, Model B) call **Predator** for inference, and compute components call **Numerix** for operations like reranking. The entire DAG topology is driven by config loaded from **etcd**.
+
+---
+
+## Core Components
+
+### 1. gRPC Server
+
+Inferflow exposes its APIs via a gRPC server, with HTTP health endpoints multiplexed on the same port using **cmux**. The server provides:
+
+- **Inferflow API** — `RetrieveModelScore`: entity-based feature retrieval and scoring
+- **Predict API** — `InferPointWise`, `InferPairWise`, `InferSlateWise`: structured inference with targets, pairs, and slates
+
+### 2. DAG Topology Executor
+
+The heart of Inferflow. Each model configuration defines a `component_dependency` map that describes a Directed Acyclic Graph (DAG) of components.
+
+**Execution model:**
+- Uses **Kahn's algorithm** for topological ordering
+- Components at the same level run **concurrently** in goroutines
+- All components share a mutable `ComponentMatrix` (rows = entity IDs, columns = features/scores)
+- DAG topologies are **cached** using Murmur3 hashing with Ristretto cache
+
+**Validation:**
+- Cycle detection via in-degree analysis
+- Component existence verification against the `ComponentProvider`
+
+### 3. Component Types
+
+Inferflow defines four types of DAG components:
+
+| Component | Role | External Dependency |
+|-----------|------|---------------------|
+| **FeatureInitComponent** | Root node — initializes the `ComponentMatrix` with entity IDs and schema | None |
+| **FeatureComponent** | Fetches features from the Online Feature Store for a specific entity type | OnFS (gRPC) |
+| **PredatorComponent** | Calls model serving endpoints for inference scoring | Predator / Helix (gRPC) |
+| **NumerixComponent** | Calls compute engine for operations like reranking | Numerix (gRPC) |
+
+### 4. ComponentMatrix — The 2D Result Matrix
+
+The ComponentMatrix is a shared, mutable 2D data structure that flows through the entire DAG. Every component reads from and writes to this matrix, progressively building a complete feature + score row for each entity.
+
+
+
+#### How the matrix evolves through the DAG
+
+The diagram above illustrates the three execution phases and how the 2D matrix grows at each stage:
+
+**Phase 1 — Feature Retrieval**
+
+The **init** node creates an empty matrix with one row per target entity ID. Feature components then execute — first the top-level entities (entity A, entity B) fetch their features from OnFS and populate their columns (shown as colored blocks). Derived entities (entity C, D, E) resolve their keys from the already-populated columns and add more feature columns. At this point the matrix contains all feature data, with each color representing features from a different entity.
+
+The right side of the diagram shows the matrix being **decomposed** — feature columns from different entities are separated into per-model input groups, selecting only the features each model needs.
+
+**Phase 2 — Model Invocation**
+
+Model X and Model Y each receive their decomposed feature slices, call **Predator** for inference, and write score columns back into the matrix (shown as new colored columns appended to the right). Multiple models can run in parallel if they don't depend on each other's outputs.
+
+The scores are then decomposed again to prepare inputs for the compute stage.
+
+**Phase 3 — Numerix Compute**
+
+The **Score Comb** node takes score columns from both models, calls **Numerix** for a final compute operation (e.g., score combination, reranking), and writes the final score column (shown in dark red) into the matrix. The result is a complete row per entity with all features and all scores.
+
+#### Matrix structure
+
+| Property | Description |
+|----------|-------------|
+| **Rows** | One per target entity ID (e.g., each product being scored) |
+| **String columns** | Human-readable values used in responses |
+| **Byte columns** | Binary-encoded feature values used for model inputs |
+| **Column naming** | `entity_label:feature_group:feature_name` |
+
+Each component only reads the columns it needs and writes to its own columns, enabling safe concurrent execution across independent branches of the DAG.
+
+For slate-based APIs, a companion `SlateData` structure holds per-slate matrices and scores, with `slate_target_indices` mapping slates to rows in the main matrix.
+
+### 5. Configuration Management (etcd)
+
+Model configurations are stored in etcd and hot-reloaded via watchers:
+
+- **Config paths**: `/config/inferflow/services/`, `/model-config`
+- **Watch mechanism**: etcd watchers trigger `ReloadModelConfigMapAndRegisterComponents` on any change
+- **On reload**: Updates `ConfigMap`, re-initializes feature schemas, and re-registers DAG components
+
+This means new models or configuration changes go live **without redeployment**.
+
+### 6. External Integrations
+
+#### Online Feature Store (OnFS)
+- gRPC client calling `FeatureService.RetrieveFeatures`
+- Batched retrieval with configurable batch size and deadline
+- Auth via `CALLER_ID` and `CALLER_TOKEN` metadata
+
+#### Predator (Model Serving)
+- Uses `go-sdk` for model inference
+- Supports **percentage-based traffic routing** across multiple model endpoints
+- Configurable calibration and batch sizing
+
+#### Numerix (Compute Engine)
+- Uses `go-sdk` Numerix client
+- RPC: `NumerixService.Compute` with entity score data
+- Used for compute operations like reranking
+
+#### Kafka (Inference Logging)
+- Async inference log publishing using `segmentio/kafka-go`
+- Supports **Proto**, **Arrow**, and **Parquet** serialization formats
+- Configurable sampling via `LoggingPerc` and user-based daily sampling
+
+---
+
+## Request Flow
+
+```
+1. Client sends gRPC request with model_config_id + entity IDs
+ │
+2. Load ModelConfig from etcd-backed ConfigMap
+ │
+3. Adapt proto request → ComponentRequest
+ (build ComponentMatrix with entity schema)
+ │
+4. Resolve DAG topology from component_dependency config
+ │
+5. Execute DAG (Kahn's algorithm, concurrent):
+ │
+ ├─ FeatureInitComponent: populate matrix with entity IDs + schema
+ │
+ ├─ FeatureComponents (parallel): fetch features from OnFS → fill matrix columns
+ │
+ ├─ PredatorComponent: build feature payloads from matrix → call model → write scores
+ │
+ └─ NumerixComponent: read scores from matrix → call compute → write final scores
+ │
+6. Build response from matrix columns per ResponseConfig
+ │
+7. (Optional) Async Kafka logging of inference features and scores
+ │
+8. Return gRPC response to client
+```
+
+---
+
+## Observability
+
+### Metrics (StatsD / Telegraf)
+
+| Metric | Description |
+|--------|-------------|
+| `inferflow.retrievemodelscore.request.total` | Total RetrieveModelScore requests |
+| `inferflow.retrievemodelscore.latency` | End-to-end latency |
+| `inferflow.retrievemodelscore.batch.size` | Batch size per request |
+| `predict.infer.request.total` | Total Predict API requests |
+| `predict.infer.latency` | Predict API latency |
+| `inferflow.component.execution.total` | Per-component execution count |
+| `inferflow.component.execution.latency` | Per-component latency |
+| `inferflow.component.execution.error` | Component-level errors |
+| `inferflow.component.feature.count` | Feature count per component |
+| `inferflow.external.api.request.total` | External API call count |
+| `inferflow.external.api.latency` | External API latency |
+| `inferflow.component.inmemorycache.request.total` | Cache hit/miss total |
+| `inferflow.component.inmemorycache.miss` | Cache misses |
+| `inferflow.logging.kafka_sent` | Kafka log messages sent |
+
+### Logging
+- Structured JSON logging via **zerolog**
+- Configurable log levels
+
+---
+
+## Deployment
+
+### Docker
+
+Inferflow ships as a multi-stage Docker image:
+
+- **Builder**: Go 1.19 Alpine with optional Kafka support (librdkafka)
+- **Runtime**: Debian 10 slim
+- **Build command**: `go build -tags musl -ldflags "-extldflags -static" -o server cmd/${module}/main.go`
+
+### Supported Environments
+- Kubernetes (K8s)
+- Google Kubernetes Engine (GKE)
+- Amazon EKS
+
+### Configuration
+All configuration is driven via environment variables (loaded by Viper) and etcd. No config files are required at deployment time.
+
+---
+
+## Target Users
+
+| User | Role |
+|------|------|
+| Data Scientists | Define model configs and feature retrieval graphs via config — no code needed |
+| ML Engineers | Onboard new models by updating etcd config; manage DAG topologies |
+| Backend Developers | Integrate via gRPC SDKs for real-time scoring in application services |
+| Platform Engineers | Deploy, scale, and monitor Inferflow clusters |
+
+---
+
+## Benefits
+
+- **No-code feature retrieval** — new models need only a config change, not custom code
+- **Feature consistency** — same graph-driven retrieval ensures identical features across experiments
+- **Faster iteration** — experiment with new models in minutes, not days
+- **Concurrent execution** — DAG components run in parallel for minimal latency
+- **Hot reloading** — model config changes via etcd go live without redeployment
+- **Multi-API support** — PointWise, PairWise, and SlateWise inference patterns out of the box
+- **Production-grade** — built in Go with gRPC, designed for millions of QPS
+
+---
+
+## Contributing
+
+We welcome contributions from the community! Please see our [Contributing Guide](https://github.com/Meesho/BharatMLStack/blob/main/CONTRIBUTING.md) for details on how to get started.
+
+## Community & Support
+
+- **Discord**: Join our [community chat](https://discord.gg/XkT7XsV2AU)
+- **Issues**: Report bugs and request features on [GitHub Issues](https://github.com/Meesho/BharatMLStack/issues)
+- **Email**: Contact us at [ml-oss@meesho.com](mailto:ml-oss@meesho.com)
+
+## License
+
+BharatMLStack is open-source software licensed under the [BharatMLStack Business Source License 1.1](https://github.com/Meesho/BharatMLStack/blob/main/LICENSE.md).
+
+---
+
+
+ Built with ❤️ for the ML community from Meesho
+
+
+ If you find this useful, ⭐️ the repo — your support means the world to us!
+
diff --git a/docs-src/docs/inferflow/v1.0.0/configuration.md b/docs-src/docs/inferflow/v1.0.0/configuration.md
new file mode 100644
index 00000000..35b9b1bd
--- /dev/null
+++ b/docs-src/docs/inferflow/v1.0.0/configuration.md
@@ -0,0 +1,366 @@
+---
+title: Configuration Guide
+sidebar_position: 3
+---
+
+# Inferflow - Configuration Guide
+
+Inferflow is fully config-driven. All model onboarding, feature retrieval logic, DAG topology, and inference behavior are controlled through configuration stored in **etcd** — with zero code changes required.
+
+---
+
+## Configuration Overview
+
+Inferflow configuration is organized into two layers:
+
+1. **Static config** — Environment variables loaded at startup (via Viper)
+2. **Dynamic config** — Model configurations stored in etcd, hot-reloaded on change
+
+---
+
+## Static Configuration (Environment Variables)
+
+These are set at deployment time and require a restart to change.
+
+### Server
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `APP_PORT` | gRPC/HTTP server port | `50051` |
+| `APP_ENV` | Environment name | `production` |
+
+### etcd
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `ETCD_ENDPOINTS` | Comma-separated etcd endpoints | `etcd-0:2379,etcd-1:2379` |
+| `ETCD_DIAL_TIMEOUT` | Connection timeout | `5s` |
+
+### Online Feature Store (OnFS)
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `externalServiceOnFs_host` | OnFS gRPC host | `onfs-api:50051` |
+| `externalServiceOnFs_callerId` | Caller ID for auth | `inferflow` |
+| `externalServiceOnFs_callerToken` | Caller token for auth | `` |
+| `externalServiceOnFs_batchSize` | Batch size for feature retrieval | `100` |
+| `externalServiceOnFs_deadline` | Request deadline | `200ms` |
+
+### Predator (Model Serving)
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `externalServicePredator_defaultDeadline` | Default inference deadline | `100ms` |
+
+### Numerix (Compute Engine)
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `numerixClientV1_host` | Numerix gRPC host | `numerix:50052` |
+| `numerixClientV1_deadline` | Request deadline | `100ms` |
+
+### Kafka (Inference Logging)
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `KafkaBootstrapServers` | Kafka broker addresses | `kafka-0:9092,kafka-1:9092` |
+| `KafkaLoggingTopic` | Topic for inference logs | `inferflow-logs` |
+
+### Metrics (StatsD / Telegraf)
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `TELEGRAF_HOST` | StatsD host | `telegraf` |
+| `TELEGRAF_PORT` | StatsD port | `8125` |
+
+### In-Memory Cache
+
+| Variable | Description | Example |
+|----------|-------------|---------|
+| `CACHE_SIZE_MB` | Cache size in MB | `512` |
+| `CACHE_TYPE` | Cache implementation | `freecache` |
+
+---
+
+## Dynamic Configuration (etcd Model Config)
+
+Model configurations are stored in etcd and hot-reloaded. Each model is identified by a `model_config_id`.
+
+### Config Structure
+
+```json
+{
+ "model_config_id_example": {
+ "dag_execution_config": {
+ "component_dependency": {
+ "feature_initializer": ["fs_user", "fs_product"],
+ "fs_user": ["ranker_model"],
+ "fs_product": ["ranker_model"],
+ "ranker_model": []
+ }
+ },
+ "component_config": {
+ "feature_component_config": {
+ "fs_user": { ... },
+ "fs_product": { ... }
+ },
+ "predator_component_config": {
+ "ranker_model": { ... }
+ },
+ "numerix_component_config": {},
+ "cache_enabled": true,
+ "cache_version": "v1",
+ "cache_ttl": 300,
+ "error_logging_percent": 10
+ },
+ "response_config": {
+ "features": ["ranker_model:score"],
+ "model_schema_perc": 100,
+ "logging_perc": 5,
+ "log_features": ["fs_user:profile:age", "ranker_model:score"],
+ "log_batch_size": 100
+ }
+ }
+}
+```
+
+---
+
+### DAG Execution Config
+
+Defines the component dependency graph.
+
+```json
+{
+ "component_dependency": {
+ "": ["", ""],
+ "": [""],
+ "": [""],
+ "": []
+ }
+}
+```
+
+**Rules:**
+- The graph must be a valid DAG (no cycles)
+- Components with no parents (zero in-degree) execute first
+- Components with empty dependency arrays `[]` are leaf nodes
+- All component names must match registered components in the `ComponentConfig`
+
+---
+
+### Feature Component Config
+
+Configures how features are fetched from the Online Feature Store.
+
+```json
+{
+ "fs_user": {
+ "fs_keys": {
+ "schema": ["user_id"],
+ "col": "context:user:user_id"
+ },
+ "fs_request": {
+ "entity_label": "user",
+ "feature_groups": [
+ {
+ "label": "demographics",
+ "feature_labels": ["age", "location", "income_bracket"]
+ },
+ {
+ "label": "behavior",
+ "feature_labels": ["click_rate", "purchase_freq"]
+ }
+ ]
+ },
+ "fs_flatten_resp_keys": ["user_id"],
+ "col_name_prefix": "user",
+ "comp_cache_enabled": true,
+ "comp_cache_ttl": 600,
+ "composite_id": false
+ }
+}
+```
+
+| Field | Description |
+|-------|-------------|
+| `fs_keys` | How to extract lookup keys from the matrix. `schema` defines key column names; `col` references a matrix column |
+| `fs_request` | OnFS query: entity label + feature groups with specific features |
+| `fs_flatten_resp_keys` | Keys to flatten in response mapping |
+| `col_name_prefix` | Prefix for matrix column names (e.g., `user:demographics:age`) |
+| `comp_cache_enabled` | Enable in-memory caching for this component |
+| `comp_cache_ttl` | Cache TTL in seconds |
+| `composite_id` | Whether entity keys are composite |
+
+---
+
+### Predator Component Config
+
+Configures model inference endpoints.
+
+```json
+{
+ "ranker_model": {
+ "model_name": "product_ranker_v3",
+ "model_endpoint": "predator-ranker:8080",
+ "model_end_points": {
+ "predator-ranker-v3:8080": 80,
+ "predator-ranker-v4:8080": 20
+ },
+ "deadline": 100,
+ "batch_size": 50,
+ "calibration": {
+ "enabled": false
+ },
+ "inputs": {
+ "feature_map": {
+ "user:demographics:age": "INT32",
+ "user:behavior:click_rate": "FP32",
+ "product:attributes:category_id": "INT32"
+ }
+ },
+ "outputs": {
+ "score_columns": ["score", "confidence"]
+ },
+ "slate_component": false
+ }
+}
+```
+
+| Field | Description |
+|-------|-------------|
+| `model_name` | Model identifier on the serving platform |
+| `model_endpoint` | Primary model serving endpoint |
+| `model_end_points` | Multiple endpoints with percentage-based traffic routing |
+| `deadline` | Inference timeout in milliseconds |
+| `batch_size` | Max items per inference batch |
+| `calibration` | Score calibration settings |
+| `inputs.feature_map` | Map of matrix column → data type for model input |
+| `outputs.score_columns` | Column names for model output scores |
+| `slate_component` | If true, runs per-slate inference |
+
+---
+
+### Numerix Component Config
+
+Configures compute operations (e.g., reranking).
+
+```json
+{
+ "reranker": {
+ "score_column": "final_score",
+ "data_type": "FP32",
+ "score_mapping": {
+ "ranker_model:score": "FP32",
+ "user:behavior:click_rate": "FP32"
+ },
+ "compute_id": "diversity_rerank_v1",
+ "slate_component": false
+ }
+}
+```
+
+| Field | Description |
+|-------|-------------|
+| `score_column` | Output column name for the computed score |
+| `data_type` | Output data type |
+| `score_mapping` | Map of matrix columns to include as compute inputs |
+| `compute_id` | Identifies the compute operation on Numerix |
+| `slate_component` | If true, runs per-slate compute |
+
+---
+
+### Response Config
+
+Controls what data is returned to the client and what is logged.
+
+```json
+{
+ "features": ["ranker_model:score", "reranker:final_score"],
+ "model_schema_perc": 100,
+ "logging_perc": 5,
+ "log_features": [
+ "user:demographics:age",
+ "ranker_model:score",
+ "reranker:final_score"
+ ],
+ "log_batch_size": 100
+}
+```
+
+| Field | Description |
+|-------|-------------|
+| `features` | Matrix columns to include in the gRPC response |
+| `model_schema_perc` | Percentage of requests that include full schema in response |
+| `logging_perc` | Percentage of requests to send to Kafka for logging |
+| `log_features` | Specific features to include in log messages |
+| `log_batch_size` | Batch size for grouped log messages |
+
+---
+
+### Service-Level Config
+
+Global settings that apply across all models.
+
+```json
+{
+ "v2_logging_type": "proto",
+ "compression_enabled": false
+}
+```
+
+| Field | Values | Description |
+|-------|--------|-------------|
+| `v2_logging_type` | `proto`, `arrow`, `parquet` | Serialization format for Kafka inference logs |
+| `compression_enabled` | `true`, `false` | Enable compression for log messages |
+
+---
+
+## Example: Onboarding a New Model
+
+To onboard a new ranking model, update the etcd config:
+
+**Step 1:** Define the feature retrieval graph
+
+```json
+"component_dependency": {
+ "feature_initializer": ["fs_user", "fs_product", "fs_user_x_category"],
+ "fs_product": ["fs_user_x_category"],
+ "fs_user": ["new_ranker"],
+ "fs_user_x_category": ["new_ranker"],
+ "new_ranker": []
+}
+```
+
+Here `fs_user_x_category` depends on `fs_product` because it needs the category ID extracted from the product entity to resolve the user x category key.
+
+**Step 2:** Configure each component (feature groups, model endpoints, etc.)
+
+**Step 3:** Push the config to etcd — Inferflow picks it up automatically via watchers.
+
+No code changes. No redeployment. The new model is live.
+
+---
+
+## Contributing
+
+We welcome contributions from the community! Please see our [Contributing Guide](https://github.com/Meesho/BharatMLStack/blob/main/CONTRIBUTING.md) for details on how to get started.
+
+## Community & Support
+
+- **Discord**: Join our [community chat](https://discord.gg/XkT7XsV2AU)
+- **Issues**: Report bugs and request features on [GitHub Issues](https://github.com/Meesho/BharatMLStack/issues)
+- **Email**: Contact us at [ml-oss@meesho.com](mailto:ml-oss@meesho.com)
+
+## License
+
+BharatMLStack is open-source software licensed under the [BharatMLStack Business Source License 1.1](https://github.com/Meesho/BharatMLStack/blob/main/LICENSE.md).
+
+---
+
+
+ Built with ❤️ for the ML community from Meesho
+
+
+ If you find this useful, ⭐️ the repo — your support means the world to us!
+
diff --git a/docs-src/docs/inferflow/v1.0.0/functionalities.md b/docs-src/docs/inferflow/v1.0.0/functionalities.md
new file mode 100644
index 00000000..cb944b1b
--- /dev/null
+++ b/docs-src/docs/inferflow/v1.0.0/functionalities.md
@@ -0,0 +1,309 @@
+---
+title: Key Functionalities
+sidebar_position: 2
+---
+
+# Inferflow - Key Functionalities
+
+## Overview
+
+Inferflow is a high-performance, config-driven ML inference orchestration engine built in **Go**. It provides **no-code feature retrieval**, **DAG-based execution**, and **multi-pattern model inference** — enabling ML teams to onboard new models through configuration changes alone.
+
+---
+
+## Core Capabilities
+
+### Graph-Driven Feature Retrieval
+
+Inferflow's defining feature is its ability to resolve entity relationships and retrieve features through configurable DAG topologies — no custom code required.
+
+**How it works:**
+
+1. A `model_config_id` maps to a pre-defined DAG of components
+2. Context entity IDs (e.g., `userId`, `productIds`) are provided at request time
+3. The DAG resolves intermediate entity relationships (e.g., extracting `category` from `product` to fetch `user x category` features)
+4. Features are fetched in parallel from the Online Feature Store
+5. A 2D feature matrix is assembled and passed to model scoring
+
+**Impact:**
+- New models require only a config update — no code changes
+- Feature consistency is guaranteed across experiments
+- Iteration cycles drop from days to minutes
+
+### DAG Topology Executor
+
+The execution engine uses **Kahn's algorithm** for topological ordering with **concurrent goroutine execution** at each level:
+
+```
+component_dependency: {
+ "feature_initializer": ["fs_user", "fs_product"],
+ "fs_user": ["ranker"],
+ "fs_product": ["ranker"],
+ "ranker": ["reranker"],
+ "reranker": []
+}
+```
+
+This config defines:
+- `feature_initializer` runs first (zero in-degree)
+- `fs_user` and `fs_product` run **in parallel** after init
+- `ranker` runs after both feature components complete
+- `reranker` runs after the ranker
+
+**Key properties:**
+- Cycle detection via in-degree analysis
+- DAG topologies cached using Murmur3 hashing (Ristretto cache)
+- Components are registered and resolved via a `ComponentProvider`
+
+---
+
+## Multi-Pattern Inference APIs
+
+Inferflow supports three inference patterns via the **Predict API**, each designed for different ML use cases:
+
+### PointWise Inference
+
+Score each target independently against context features.
+
+```protobuf
+rpc InferPointWise(PredictRequest) returns (PredictResponse);
+```
+
+**Use cases:** Click-through rate prediction, fraud scoring, relevance ranking
+
+**Input:** Context features + list of targets (e.g., products)
+**Output:** Per-target scores
+
+### PairWise Inference
+
+Score pairs of targets relative to each other.
+
+```protobuf
+rpc InferPairWise(PredictRequest) returns (PredictResponse);
+```
+
+**Use cases:** Preference learning, comparison-based ranking
+
+**Input:** Context features + targets + pair indices (first/second)
+**Output:** Per-pair scores + optional per-target scores
+
+### SlateWise Inference
+
+Score groups (slates) of targets together, capturing inter-item effects.
+
+```protobuf
+rpc InferSlateWise(PredictRequest) returns (PredictResponse);
+```
+
+**Use cases:** Whole-page optimization, slate-level reranking, diversity-aware scoring
+
+**Input:** Context features + targets + slate definitions (target indices per slate)
+**Output:** Per-slate scores + optional per-target scores
+
+---
+
+## Entity & Legacy API
+
+### RetrieveModelScore
+
+The original Inferflow API for entity-based feature retrieval and scoring:
+
+```protobuf
+service Inferflow {
+ rpc RetrieveModelScore(InferflowRequestProto) returns (InferflowResponseProto);
+}
+```
+
+**Request structure:**
+
+| Field | Description |
+|-------|-------------|
+| `entities` | List of entity types with their IDs and optional inline features |
+| `model_config_id` | Identifies the model configuration (DAG, components, response format) |
+| `tracking_id` | Request-level tracing identifier |
+
+**Entity structure:**
+- `entity`: Entity type label (e.g., `"user"`, `"product"`)
+- `ids`: List of entity IDs
+- `features`: Optional inline features (name + per-ID values)
+
+---
+
+## Component Types
+
+### FeatureInitComponent
+
+**Role:** Root DAG node — initializes the shared `ComponentMatrix`.
+
+- Sets up rows from entity IDs
+- Populates schema columns (string + byte) for all downstream components
+- For slate APIs: initializes `SlateData` with `slate_target_indices`
+
+### FeatureComponent
+
+**Role:** Fetches features from the Online Feature Store (OnFS) for a specific entity type.
+
+- Reads `FSKeys` from config to extract lookup keys from the matrix
+- Batches unique entities and calls OnFS via gRPC
+- Optional **in-memory caching** keyed by `model_id:version:component:entity`
+- Writes binary feature values into matrix byte columns
+
+**Column naming convention:** `entity_label:feature_group:feature_name`
+
+### PredatorComponent
+
+**Role:** Calls model serving endpoints for inference.
+
+- Builds feature payloads from matrix columns with type conversion
+- Supports **percentage-based traffic routing** across multiple model endpoints
+- Handles **slate-level inference**: per-slate matrix → separate inference → scores to `SlateData`
+- Configurable **calibration** and **batch sizing**
+
+### NumerixComponent
+
+**Role:** Calls the Numerix compute engine for operations like reranking.
+
+- Uses `ScoreMapping` config to map matrix columns to compute inputs
+- Writes a single score column back to the matrix
+- Supports slate mode for per-slate compute operations
+
+---
+
+## Feature Retrieval Pipeline
+
+### Key Resolution
+
+Feature components use `FSKeys` configuration to dynamically resolve entity keys:
+
+```json
+{
+ "FSKeys": {
+ "schema": ["user_id"],
+ "col": "user:profile:user_id"
+ }
+}
+```
+
+The component reads key values from the existing matrix columns, enabling **chained entity resolution** — e.g., fetch product entity first, extract category, then fetch user x category features.
+
+### Batched Retrieval
+
+- Features are fetched via `FeatureService.RetrieveFeatures` gRPC call
+- Requests are batched by unique entity keys
+- Configurable batch size and deadline per component
+- Auth via `CALLER_ID` and `CALLER_TOKEN` metadata
+
+### In-Memory Caching
+
+Optional per-component caching reduces OnFS load:
+
+- Cache key: `model_id:cache_version:component_name:entity_key`
+- Configurable TTL per component
+- Zero-GC-overhead cache implementation available
+- Cache hit/miss metrics tracked via StatsD
+
+---
+
+## Data Types
+
+Inferflow supports comprehensive ML data types for feature encoding and model input/output:
+
+| Data Type | Variants | Usage |
+|-----------|----------|-------|
+| **Integers** | int8, int16, int32, int64 | Categorical encodings, counts, IDs |
+| **Floats** | float8 (e4m3, e5m2), float16, float32, float64 | Continuous features, embeddings, scores |
+| **Strings** | Variable length | Categories, metadata |
+| **Booleans** | Bit-packed | Binary indicators |
+| **Vectors** | All scalar types | Embeddings, feature arrays |
+
+Type conversion is handled by the `datatypeconverter` package with optimized float8 implementations.
+
+---
+
+## Inference Logging
+
+Inferflow supports async inference logging to Kafka for model monitoring and debugging:
+
+### Serialization Formats
+
+| Format | Use Case |
+|--------|----------|
+| **Proto** | Default, compact |
+| **Arrow** | Columnar analytics |
+| **Parquet** | Long-term storage, query-friendly |
+
+### Sampling Controls
+
+| Config | Description |
+|--------|-------------|
+| `LoggingPerc` | Percentage of requests to log (0-100) |
+| `LogBatchSize` | Batch size for log message grouping |
+| `LogFeatures` | Specific features to include in logs |
+
+### Log Content
+
+Each `InferflowLog` message includes:
+- `user_id`, `tracking_id`, `model_config_id`
+- Entity IDs and feature values
+- Model scores and metadata
+
+---
+
+## Configuration Hot-Reload
+
+Model configurations are stored in **etcd** and support **live updates without redeployment**:
+
+1. Inferflow registers watchers on etcd config paths
+2. On config change, watchers trigger `ReloadModelConfigMapAndRegisterComponents`
+3. `ConfigMap` is updated in memory
+4. Feature schemas are re-initialized
+5. DAG components are re-registered
+
+This enables:
+- Adding new models in production without restarts
+- A/B testing with different model configurations
+- Instant rollback by reverting etcd config
+
+---
+
+## Performance Characteristics
+
+### Concurrency Model
+- DAG components at the same level execute concurrently in goroutines
+- Feature retrieval is parallelized across entity types
+- External gRPC calls use connection pooling
+
+### Memory Efficiency
+- Built in Go — significantly lower memory footprint than Java equivalents (~80% reduction)
+- Object pooling for `ComponentMatrix` and serialization buffers
+- In-memory cache with zero-GC-overhead option (freecache)
+
+### Serialization
+- gRPC with Proto3 for all external communication
+- Binary feature encoding in the `ComponentMatrix` for minimal overhead
+- Configurable compression for Kafka logging
+
+---
+
+## Contributing
+
+We welcome contributions from the community! Please see our [Contributing Guide](https://github.com/Meesho/BharatMLStack/blob/main/CONTRIBUTING.md) for details on how to get started.
+
+## Community & Support
+
+- **Discord**: Join our [community chat](https://discord.gg/XkT7XsV2AU)
+- **Issues**: Report bugs and request features on [GitHub Issues](https://github.com/Meesho/BharatMLStack/issues)
+- **Email**: Contact us at [ml-oss@meesho.com](mailto:ml-oss@meesho.com)
+
+## License
+
+BharatMLStack is open-source software licensed under the [BharatMLStack Business Source License 1.1](https://github.com/Meesho/BharatMLStack/blob/main/LICENSE.md).
+
+---
+
+
+ Built with ❤️ for the ML community from Meesho
+
+
+ If you find this useful, ⭐️ the repo — your support means the world to us!
+
diff --git a/docs-src/docs/inferflow/v1.0.0/release-notes.md b/docs-src/docs/inferflow/v1.0.0/release-notes.md
new file mode 100644
index 00000000..2a73c843
--- /dev/null
+++ b/docs-src/docs/inferflow/v1.0.0/release-notes.md
@@ -0,0 +1,208 @@
+---
+title: Release Notes
+sidebar_position: 4
+---
+
+# Inferflow - Release Notes
+
+## Version 1.0.0
+**Release Date**: June 2025
+**Status**: General Availability (GA)
+
+We're excited to announce the first stable release of **Inferflow** — a graph-driven feature retrieval and model inference orchestration engine, part of BharatMLStack.
+
+---
+
+## What's New
+
+### Config-Driven DAG Executor
+- **No-code feature retrieval**: Onboard new models with config changes only — no custom code required
+- **DAG topology execution**: Define component dependency graphs that are executed concurrently using Kahn's algorithm
+- **Hot reload**: Model configurations stored in etcd are watched and reloaded live — no redeployment needed
+- **DAG caching**: Topologies are cached using Murmur3 hashing with Ristretto for minimal overhead
+
+### Multi-Pattern Inference APIs
+Three structured inference patterns via the Predict API:
+
+| API | Pattern | Use Case |
+|-----|---------|----------|
+| `InferPointWise` | Score each target independently | CTR prediction, fraud scoring |
+| `InferPairWise` | Score pairs of targets | Preference learning, comparison ranking |
+| `InferSlateWise` | Score groups of targets together | Whole-page optimization, diversity-aware ranking |
+
+Plus the entity-based `RetrieveModelScore` API for direct feature retrieval and scoring.
+
+### Component System
+Four built-in component types:
+- **FeatureInitComponent** — Initializes the shared ComponentMatrix
+- **FeatureComponent** — Fetches features from the Online Feature Store (OnFS)
+- **PredatorComponent** — Calls model serving endpoints with percentage-based traffic routing
+- **NumerixComponent** — Calls compute engine for operations like reranking
+
+### Online Feature Store Integration
+- gRPC-based feature retrieval via `FeatureService.RetrieveFeatures`
+- Batched retrieval with configurable batch size and deadline
+- Token-based authentication
+- Dynamic key resolution from the ComponentMatrix
+
+### In-Memory Feature Caching
+- Optional per-component caching to reduce OnFS load
+- Configurable TTL per component
+- Zero-GC-overhead cache option (freecache)
+- Cache hit/miss metrics
+
+### Inference Logging
+- Async logging to Kafka for model monitoring and debugging
+- Three serialization formats: **Proto**, **Arrow**, **Parquet**
+- Configurable sampling rate and feature selection
+- Batched log message grouping
+
+---
+
+## Performance
+
+### Built in Go
+Inferflow is written entirely in Go, delivering:
+- ~80% lower memory usage compared to equivalent Java services
+- Lower CPU utilization
+- Faster, more efficient deployments
+
+### Concurrency
+- DAG components at the same level execute concurrently in goroutines
+- Feature retrieval parallelized across entity types
+- Connection pooling for all external gRPC calls
+
+### Serialization
+- gRPC with Proto3 for all APIs
+- Binary feature encoding in the ComponentMatrix
+- Configurable compression for Kafka logging (ZSTD support)
+
+---
+
+## APIs & Protocols
+
+### gRPC API
+
+**Inferflow Service:**
+```protobuf
+service Inferflow {
+ rpc RetrieveModelScore(InferflowRequestProto) returns (InferflowResponseProto);
+}
+```
+
+**Predict Service:**
+```protobuf
+service PredictService {
+ rpc InferPointWise(PredictRequest) returns (PredictResponse);
+ rpc InferPairWise(PredictRequest) returns (PredictResponse);
+ rpc InferSlateWise(PredictRequest) returns (PredictResponse);
+}
+```
+
+### Data Types Supported
+
+| Type | Variants |
+|------|----------|
+| Integers | int8, int16, int32, int64 |
+| Floats | float8 (e4m3, e5m2), float16, float32, float64 |
+| Strings | Variable length |
+| Booleans | Bit-packed |
+| Vectors | All scalar types |
+
+---
+
+## Enterprise Features
+
+### Production Readiness
+- **Health checks**: HTTP health endpoints via cmux
+- **Graceful shutdown**: Clean resource cleanup
+- **Structured logging**: JSON-formatted logs via zerolog
+- **Signal handling**: SIGTERM/SIGINT support for container environments
+
+### Monitoring & Observability
+- **StatsD / Telegraf integration**: Request rates, latencies, error rates
+- **Per-component metrics**: Execution time, feature counts, cache hit rates
+- **External API metrics**: OnFS, Predator, Numerix call tracking
+- **Kafka logging metrics**: Messages sent, errors
+
+### Configuration Management
+- **etcd-based**: All model configs stored in etcd
+- **Watch & reload**: Live config updates without restart
+- **Multi-model support**: Multiple `model_config_id` entries served concurrently
+
+---
+
+## Deployment
+
+### Container Support
+- **Docker image**: Multi-stage build (Go Alpine builder + Debian runtime)
+- **Optional Kafka**: librdkafka support via build flag
+- **Static binary**: Single binary deployment
+
+### Supported Environments
+- Kubernetes (K8s)
+- Google Kubernetes Engine (GKE)
+- Amazon EKS
+
+---
+
+## Compatibility
+
+### Supported Go Versions
+- **Minimum**: Go 1.19
+- **Recommended**: Go 1.24+
+
+### External Dependencies
+
+| Service | Version | Protocol |
+|---------|---------|----------|
+| etcd | 3.5+ | gRPC |
+| Online Feature Store (OnFS) | 1.0+ | gRPC |
+| Predator (Helix) | 1.0+ | gRPC |
+| Numerix | 1.0+ | gRPC |
+| Kafka | 2.0+ | TCP |
+
+---
+
+## Download & Installation
+
+### Source Code
+```bash
+git clone https://github.com/Meesho/BharatMLStack.git
+cd BharatMLStack/inferflow
+```
+
+### Build
+```bash
+go build -o inferflow-server cmd/inferflow/main.go
+```
+
+### Docker
+```bash
+docker build -t inferflow:latest .
+```
+
+---
+
+## Contributing
+
+We welcome contributions from the community! Please see our [Contributing Guide](https://github.com/Meesho/BharatMLStack/blob/main/CONTRIBUTING.md) for details on how to get started.
+
+## Community & Support
+
+- **Discord**: Join our [community chat](https://discord.gg/XkT7XsV2AU)
+- **Issues**: Report bugs and request features on [GitHub Issues](https://github.com/Meesho/BharatMLStack/issues)
+- **Email**: Contact us at [ml-oss@meesho.com](mailto:ml-oss@meesho.com)
+
+## License
+
+BharatMLStack is open-source software licensed under the [BharatMLStack Business Source License 1.1](https://github.com/Meesho/BharatMLStack/blob/main/LICENSE.md).
+
+---
+
+
+ Built with ❤️ for the ML community from Meesho
+
+
+ If you find this useful, ⭐️ the repo — your support means the world to us!
+
diff --git a/docs-src/package-lock.json b/docs-src/package-lock.json
deleted file mode 100644
index c2617028..00000000
--- a/docs-src/package-lock.json
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diff --git a/docs-src/src/pages/index.js b/docs-src/src/pages/index.js
index d2d965fd..c58e5f55 100644
--- a/docs-src/src/pages/index.js
+++ b/docs-src/src/pages/index.js
@@ -167,6 +167,43 @@ function SDKsAbout() {
);
}
+function NumerixAbout() {
+ return (
+
+
+
+
+
Numerix
+
+ Numerix is a mathematical compute engine for BharatML Stack. It is used to perform mathematical operations on matrices and vectors.
+
+
+ Explore Numerix →
+
+
+
+
+
🛠️ Numerix Features
+
+ ✅ Postfix expression evaluation
+ ✅ Vectorized math operations
+ ✅ Typed evaluation
+ ✅ Compiler-assisted SIMD
+ ✅ ARM & AMD support
+ ✅ Multi-arch builds
+ ✅ Deterministic runtime
+
+
+
+
+
+
+
+ );
+}
+
export default function Home() {
const {siteConfig} = useDocusaurusContext();
return (
@@ -181,6 +218,7 @@ export default function Home() {
+
);
diff --git a/docs-src/static/img/v1.0.0-inferflow-arch.png b/docs-src/static/img/v1.0.0-inferflow-arch.png
new file mode 100644
index 00000000..acad4888
Binary files /dev/null and b/docs-src/static/img/v1.0.0-inferflow-arch.png differ
diff --git a/docs-src/static/img/v1.0.0-inferflow-dag-matrix.png b/docs-src/static/img/v1.0.0-inferflow-dag-matrix.png
new file mode 100644
index 00000000..2345d69f
Binary files /dev/null and b/docs-src/static/img/v1.0.0-inferflow-dag-matrix.png differ
diff --git a/docs-src/yarn.lock b/docs-src/yarn.lock
index a0cac9b6..c1e7229f 100644
--- a/docs-src/yarn.lock
+++ b/docs-src/yarn.lock
@@ -6185,7 +6185,7 @@ mrmime@^2.0.0:
ms@2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/ms/-/ms-2.0.0.tgz#5608aeadfc00be6c2901df5f9861788de0d597c8"
- integrity sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7numerixpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A==
+ integrity sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A==
ms@2.1.3, ms@^2.1.3:
version "2.1.3"
@@ -6729,7 +6729,7 @@ postcss-gap-properties@^6.0.0:
postcss-image-set-function@^7.0.0:
version "7.0.0"
resolved "https://registry.yarnpkg.com/postcss-image-set-function/-/postcss-image-set-function-7.0.0.tgz#538e94e16716be47f9df0573b56bbaca86e1da53"
- integrity sha512-QL7W7QNlZuzOwBTeXEmbVckNt1FSmhQtbMRvGGqqU4Nf4xk6KUEQhAoWuMzwbSv5jxnumerixZ5Tv7eiDB9U87znA==
+ integrity sha512-QL7W7QNlZuzOwBTeXEmbVckNt1FSmhQtbMRvGGqqU4Nf4xk6KUEQhAoWuMzwbSv5jxiRiSZ5Tv7eiDB9U87znA==
dependencies:
"@csstools/utilities" "^2.0.0"
postcss-value-parser "^4.2.0"
diff --git a/docs/404.html b/docs/404.html
index 4d1bd7ff..a131da18 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -4,14 +4,14 @@
BharatMLStack
-
-
-
+
+
+
-Page Not Found We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken.
+Page Not Found We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken.