AI Engineer Learning Path

I've been a software engineer for quite some time now, and like many of us in the industry, I realised that AI isn't just a buzzword anymore - it's becoming essential. So I put together this learning roadmap for myself (and anyone else in a similar boat) to make this transition systematically.

The idea is simple: we already know how to code, we understand software architecture, and we've shipped production systems. Now we just need to bridge the gap to AI/ML. This isn't about starting from scratch - it's about building on what we already know.

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Phase 1: Getting the Basics Right (Weeks 1-4)

Honestly, this is the part most of us dread - going back to maths! But trust me, you don't need a PhD. Just enough to understand what's happening under the hood.

1.1 AI/ML Fundamentals

• Maths Refresher

  • Linear Algebra (vectors, matrices - basically how ML sees data)
  • Calculus (derivatives, gradients - how models learn)
  • Probability & Statistics (the backbone of ML predictions)

  Videos That Actually Make Sense:

  • 3Blue1Brown - Essence of Linear Algebra
  • 3Blue1Brown - Essence of Calculus
  • StatQuest - Statistics Fundamentals
  • Khan Academy - Linear Algebra

  Articles:

  • Mathematics for Machine Learning Book (Free PDF)
  • The Matrix Calculus You Need For Deep Learning

• Machine Learning Basics (The actual fun part begins here)

  • Supervised vs Unsupervised Learning - know the difference, it comes up everywhere
  • Classification, Regression, Clustering - your bread and butter
  • Model evaluation metrics (precision, recall, F1, AUC) - how to know if your model is rubbish or not
  • Overfitting, underfitting, cross-validation - common pitfalls we all face

  Videos Worth Your Time:

  • Andrew Ng's Machine Learning Specialization
  • StatQuest - Machine Learning
  • Sentdex - Machine Learning with Python

  Reading Material:

  • Google's Machine Learning Crash Course
  • ML.NET Documentation
  • Scikit-learn User Guide

1.2 Python for AI

Most AI research, tutorials, and libraries are Python-first. Think of it as adding another tool to your belt.

• Python Essentials (It's easier than you think, coming from C#)

  • Python syntax - you'll pick it up fast, trust me
  • NumPy, Pandas - these are genuinely brilliant for data work
  • Jupyter Notebooks - interactive coding, great for experimentation
  • Virtual environments (venv, conda) - package management sorted

  Videos I Found Helpful:

  • Corey Schafer - Python Tutorials
  • freeCodeCamp - Python for Data Science
  • NumPy Tutorial - Keith Galli
  • Pandas Tutorial - Corey Schafer

  Articles & Docs:

  • RealPython
  • NumPy Quickstart
  • 10 Minutes to Pandas

• C# ML Ecosystem (Our home ground!)

  • ML.NET - Microsoft's own ML framework, works great with .NET
  • ONNX Runtime - run models trained in Python inside your C# apps
  • TorchSharp - PyTorch but in C#, pretty neat
  • Semantic Kernel - this is the real deal for building AI apps

  Videos:

  • ML.NET Tutorial - Microsoft
  • .NET AI with Semantic Kernel
  • TorchSharp Getting Started

  Documentation:

  • ML.NET Docs
  • Semantic Kernel Docs
  • ONNX Runtime for .NET

---

Phase 2: Deep Learning - Where the Magic Happens (Weeks 5-10)

This is where it gets exciting. Neural networks, deep learning - the stuff that powers ChatGPT, image recognition, and all those fancy AI applications we see daily.

2.1 Neural Network Fundamentals

• Core Concepts (Get these right, and everything else will make sense)

  • Perceptrons and activation functions - the building blocks
  • Backpropagation and gradient descent - how networks learn (this took me a while to truly get)
  • Loss functions and optimizers - guiding the learning process
  • Regularisation techniques (dropout, batch norm) - preventing overfitting

  Must-Watch Videos:

  • 3Blue1Brown - Neural Networks
  • Andrej Karpathy - Neural Networks: Zero to Hero
  • StatQuest - Neural Networks
  • Deep Learning Fundamentals - deeplizard

  Reading:

  • Neural Networks and Deep Learning (Free Book)
  • CS231n Course Notes (Stanford)
  • Colah's Blog

• Architectures (Know when to use what)

  • Feedforward Neural Networks - the simplest form
  • Convolutional Neural Networks (CNNs) - brilliant for images
  • Recurrent Neural Networks (RNNs, LSTMs) - for sequential data
  • Attention mechanisms - the foundation of modern AI, this changed everything

  University-Level Content (Free!):

  • MIT 6.S191 - Intro to Deep Learning
  • Stanford CS231n - CNNs for Visual Recognition
  • Stanford CS224n - NLP with Deep Learning
  • Attention Mechanism Explained - Rasa

  Articles That Clicked for Me:

  • Understanding LSTMs - Colah
  • The Illustrated Transformer - Jay Alammar
  • CNN Explainer - Interactive

2.2 Deep Learning Frameworks

You'll need to pick at least one. My recommendation? Start with PyTorch - it's more intuitive and the debugging is much easier.

• PyTorch (My recommendation - more Pythonic, easier to debug)

  • Tensors and autograd - the core concepts
  • Building custom models - where you get creative
  • Training loops and evaluation - the practical bits

  Video Resources:

  • PyTorch Official Tutorials
  • freeCodeCamp - PyTorch for Deep Learning
  • Aladdin Persson - PyTorch Tutorials
  • Daniel Bourke - Learn PyTorch

  Reading:

  • PyTorch 60 Minute Blitz
  • Learn PyTorch (Free Book)
  • PyTorch Lightning Docs

• TensorFlow/Keras (Good for production, widely used in industry)

  • Keras APIs - Sequential and Functional
  • TensorFlow Serving - for deployment

  Video Tutorials:

  • TensorFlow in 10 Hours - freeCodeCamp
  • Keras with TensorFlow - Deeplizard
  • TensorFlow Developer Certificate Prep

  Articles:

  • TensorFlow Official Tutorials
  • Keras Documentation

• .NET Deep Learning

  • TorchSharp for PyTorch in C#
  • ONNX model inference in .NET
  • ML.NET deep learning integrations

  Video Tutorials:

  • TorchSharp Tutorial - .NET Foundation
  • ONNX Runtime in .NET

  Articles:

  • TorchSharp GitHub Examples
  • ONNX Runtime C# API
  • Deep Learning with ML.NET

---

Phase 3: Large Language Models & NLP (Weeks 11-18)

This is where things get really interesting. LLMs are what's driving the current AI revolution - ChatGPT, Claude, Gemini. Understanding how these work will set you apart.

3.1 NLP Foundations

• Text Processing (The basics before we go deep)

  • Tokenisation, stemming, lemmatisation - how computers understand text
  • Word embeddings (Word2Vec, GloVe) - representing words as numbers
  • Sequence-to-sequence models - the precursor to modern LLMs

  Videos:

  • Stanford CS224n - NLP with Deep Learning
  • Hugging Face NLP Course
  • NLP Zero to Hero - TensorFlow
  • Word Embeddings Explained - Luis Serrano

  Reading:

  • The Illustrated Word2Vec - Jay Alammar
  • Speech and Language Processing (Free Book)
  • spaCy 101

• Transformer Architecture (This is THE architecture - everything modern is based on this)

  • Self-attention mechanism - the key innovation
  • Multi-head attention - parallelising attention
  • Positional encoding - how transformers understand sequence order
  • Encoder-decoder architecture - the original transformer design

  Videos That Made It Click:

  • Attention Is All You Need - Yannic Kilcher
  • Transformer Neural Networks - CodeEmporium
  • Let's Build GPT - Andrej Karpathy
  • Transformers Explained - StatQuest

  Must-Read Articles:

  • The Illustrated Transformer - Jay Alammar
  • The Illustrated GPT-2 - Jay Alammar
  • Attention? Attention! - Lil'Log

3.2 Modern LLMs

• Understanding LLMs (Know what's under the hood)

  • GPT architecture and variants - the decoder-only approach
  • BERT and bidirectional models - good for understanding text
  • Scaling laws and emergent capabilities - why bigger sometimes means smarter
  • Context windows and tokenisation - practical limitations you'll encounter

  Videos:

  • Andrej Karpathy - Intro to LLMs (1hr talk)
  • State of GPT - Andrej Karpathy
  • Large Language Models - MIT
  • BERT Explained - CodeEmporium

  Reading:

  • The Illustrated GPT-2 - Jay Alammar
  • BERT Explained - Jay Alammar
  • LLM Survey Paper
  • LLM Visualisation (Interactive)

• Working with LLM APIs (The practical stuff - this is what you'll use daily)

  • Azure OpenAI Service - enterprise-grade, good for production
  • OpenAI API - the original, still very good
  • Anthropic Claude API - my personal favourite for coding tasks
  • Google Gemini API - solid alternative
  • Local models (Ollama, LM Studio) - run LLMs on your machine, no API costs!

  Videos:

  • OpenAI API Tutorial - freeCodeCamp
  • Azure OpenAI Service - Microsoft
  • Ollama Tutorial - NetworkChuck
  • Claude API Tutorial - Anthropic

  Documentation:

  • OpenAI API Docs
  • Azure OpenAI Quickstart
  • Anthropic Claude Docs
  • Ollama

3.3 Prompt Engineering

This is a skill in itself. Good prompts can make average models perform brilliantly. Bad prompts will frustrate you endlessly.

• Techniques (Master these, they're genuinely useful)

  • Zero-shot, few-shot learning - teaching the model with examples
  • Chain-of-thought prompting - making the model "think" step by step
  • ReAct pattern - reasoning and acting together
  • Structured outputs (JSON mode) - getting predictable formats
  • System prompts and personas - setting context and behaviour

  Videos:

  • ChatGPT Prompt Engineering - DeepLearning.AI
  • Advanced Prompt Engineering - Elvis Saravia
  • Chain of Thought Prompting Explained
  • Prompt Engineering Full Course - freeCodeCamp

  Reading:

  • Prompt Engineering Guide
  • OpenAI Prompt Engineering Guide
  • Anthropic Prompt Engineering
  • Chain-of-Thought Paper

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Phase 4: Building Real AI Applications (Weeks 19-26)

Alright, now we get to build actual stuff! This is where your software engineering background really shines. We know how to ship products - now let's ship AI products.

4.1 Semantic Kernel & AI Orchestration

If you're coming from .NET, Semantic Kernel is your best friend. Microsoft built this specifically for us.

• Microsoft Semantic Kernel (Essential for .NET AI apps)

  • Kernels, plugins, and functions - the building blocks
  • Planners and automatic orchestration - let AI decide how to solve problems
  • Memory and embeddings - giving your app context
  • Connectors (OpenAI, Azure, Hugging Face) - plug and play with different providers

  Videos:

  • Semantic Kernel Explained - Microsoft
  • Building AI Apps with Semantic Kernel
  • Semantic Kernel Deep Dive - John Maeda
  • .NET AI Apps - Scott Hanselman

  Documentation:

  • Semantic Kernel Docs
  • Semantic Kernel Samples
  • Quickstart Guide

// Example: Basic Semantic Kernel setup
using Microsoft.SemanticKernel;

var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        deploymentName: "gpt-4",
        endpoint: "https://your-resource.openai.azure.com",
        apiKey: "your-key")
    .Build();

var result = await kernel.InvokePromptAsync("Explain quantum computing simply");

4.2 Retrieval-Augmented Generation (RAG)

RAG is probably the most practical AI pattern you'll implement. It lets you give LLMs access to your own data without expensive fine-tuning. Think of it as "teaching" the model about your documents at runtime.

• Vector Databases (Where you store your embeddings)

  • Embeddings and similarity search - the core concept
  • Azure AI Search - Microsoft's offering, integrates well with Azure
  • Pinecone, Weaviate, Qdrant - popular managed options
  • Chroma, FAISS - for local development and testing

  Videos:

  • RAG Explained - IBM Technology
  • Vector Databases Explained - Fireship
  • Building RAG Applications - DeepLearning.AI
  • Pinecone RAG Tutorial

  Reading:

  • RAG Survey Paper
  • Azure AI Search Docs
  • Pinecone Learning Centre
  • Chroma Docs

• RAG Implementation (The nitty-gritty)

  • Document chunking strategies - how to split your documents
  • Embedding generation - converting text to vectors
  • Hybrid search (keyword + semantic) - best of both worlds
  • Re-ranking and filtering - improving result quality

  Videos:

  • Advanced RAG Techniques - LangChain
  • RAG from Scratch - LangChain
  • Semantic Kernel RAG Tutorial

  Reading:

  • Chunking Strategies for RAG
  • LangChain RAG Tutorial
  • RAG with Semantic Kernel

// Example: RAG with Semantic Kernel
#pragma warning disable SKEXP0001
var memoryBuilder = new MemoryBuilder()
    .WithAzureOpenAITextEmbeddingGeneration("text-embedding-ada-002", endpoint, apiKey)
    .WithMemoryStore(new VolatileMemoryStore())
    .Build();

await memoryBuilder.SaveInformationAsync("docs", "Your document content", "doc1");
var results = await memoryBuilder.SearchAsync("docs", "query", limit: 5);

4.3 AI Agents & Autonomous Systems

This is the frontier right now. Agents can use tools, reason about problems, and take actions. It's like giving the LLM hands and feet.

• Agent Fundamentals (Understand the concepts first)

  • Tool use and function calling - letting AI use your APIs
  • Agent loops and reasoning - how agents "think"
  • Multi-agent systems - multiple AI agents working together
  • Planning and task decomposition - breaking complex problems down

  Videos:

  • Building AI Agents - DeepLearning.AI
  • What are AI Agents? - IBM Technology
  • Multi-Agent Systems - DeepLearning.AI
  • Function Calling Explained - Sam Witteveen

  Reading:

  • LLM Powered Autonomous Agents - Lil'Log
  • OpenAI Function Calling Guide
  • Building LLM Agents - LangChain
  • ReAct Pattern Paper

• Building Agents in .NET (Our territory)

  • Azure AI Agent Service - Microsoft's managed service
  • AutoGen for .NET - Multi-agent framework
  • Custom agent implementations - when you need full control

  Videos:

  • AutoGen Tutorial - Microsoft
  • Multi-Agent AI with AutoGen
  • Building Agents with Semantic Kernel

  Documentation:

  • AutoGen Docs
  • Azure AI Agent Service
  • Semantic Kernel Agents

// Example: Function calling with Semantic Kernel
var getWeatherFunc = kernel.CreateFunctionFromMethod(
    (string city) => $"Weather in {city}: Sunny, 72°F",
    "GetWeather",
    "Gets the current weather for a city");

kernel.Plugins.AddFromFunctions("Weather", [getWeatherFunc]);

---

Phase 5: MLOps & Production AI (Weeks 27-32)

As software engineers, we know that building is one thing - running in production is another. MLOps is where we apply our DevOps knowledge to ML systems.

5.1 Model Training & Fine-tuning

• Fine-tuning Techniques (When pre-trained isn't enough)

  • Transfer learning - use what's already been learnt
  • LoRA and QLoRA - efficient fine-tuning without breaking the bank
  • Prompt tuning - softer approach to customisation
  • RLHF basics - how ChatGPT became so helpful

  Videos:

  • Fine-tuning LLMs - DeepLearning.AI
  • LoRA Explained - Umar Jamil
  • QLoRA Fine-tuning Tutorial
  • RLHF Explained - Hugging Face

  Reading:

  • Hugging Face PEFT Docs
  • Fine-tuning Guide - Hugging Face
  • LoRA Paper
  • RLHF Blog - Hugging Face

• Training Infrastructure (The expensive bits)

  • GPU/TPU computing - you'll need these for training
  • Distributed training - when one GPU isn't enough
  • Azure Machine Learning - Microsoft's ML platform
  • Weights & Biases - experiment tracking (genuinely useful)

  Videos:

  • Azure Machine Learning Tutorial
  • Weights & Biases Tutorial
  • Distributed Training - PyTorch

  Documentation:

  • Azure ML Docs
  • Weights & Biases Quickstart
  • Distributed Training Guide - PyTorch

5.2 Model Deployment

• Deployment Strategies (Getting models into production)

  • Model serving (TensorFlow Serving, TorchServe) - dedicated serving solutions
  • ONNX for cross-platform deployment - train in Python, deploy in C#
  • Containerisation (Docker) - we know this already
  • Kubernetes for scaling - again, familiar territory

  Video Tutorials:

  • ML Model Deployment - Krish Naik
  • Docker for ML - freeCodeCamp
  • TorchServe Tutorial
  • ONNX Runtime Deployment

  Articles:

  • ML Deployment Guide - Made With ML
  • TorchServe Documentation
  • ONNX Runtime Performance

• Azure AI Services

  • Azure OpenAI Service
  • Azure Machine Learning endpoints
  • Azure AI Foundry
  • Azure Cognitive Services

  Video Tutorials:

  • Azure AI Services Overview - Microsoft
  • Azure ML Endpoints Tutorial
  • Azure AI Foundry (AI Studio)

  Articles:

  • Azure AI Services Documentation
  • Azure OpenAI Service
  • Azure AI Foundry Documentation

5.3 MLOps Best Practices

This is where your software engineering experience becomes a superpower. CI/CD, testing, monitoring - we've done this before, just with a twist.

• CI/CD for ML (DevOps meets ML)

  • Model versioning (MLflow, DVC) - like Git, but for models and data
  • Automated testing for ML - it's trickier than regular testing
  • Model monitoring and drift detection - models degrade over time
  • A/B testing for models - which version performs better?

  Videos:

  • MLOps Course - Made With ML
  • MLflow Tutorial - Databricks
  • DVC Tutorial - Iterative
  • ML Testing - Google

  Reading:

  • MLOps Guide - Google
  • MLflow Docs
  • DVC Docs
  • ML Test Score - Google Paper

# Example: Azure ML Pipeline (YAML)
$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
display_name: training-pipeline
jobs:
  train:
    type: command
    code: ./src
    command: python train.py
    environment: azureml:AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest

---

Phase 6: Advanced Topics (Weeks 33-40)

By now you've got a solid foundation. This phase is about exploring specialisations and staying ahead of the curve.

6.1 Multimodal AI

AI isn't just about text anymore. Images, audio, video - everything's coming together.

• Vision-Language Models (AI that can "see")

  • GPT-4 Vision - image understanding built into GPT
  • CLIP and image embeddings - connecting images and text
  • Document AI and OCR - extracting text from documents

  Video Tutorials:

  • Multimodal LLMs Explained - AI Jason
  • CLIP Explained - Yannic Kilcher
  • GPT-4 Vision Tutorial
  • Document AI with Azure

  Articles:

  • CLIP Paper - OpenAI
  • GPT-4 Vision Guide
  • Azure Document Intelligence
  • Multimodal AI Survey

• Audio & Speech

  • Speech-to-text (Whisper)
  • Text-to-speech
  • Audio processing

  Video Tutorials:

  • OpenAI Whisper Tutorial - Assembly AI
  • Azure Speech Services
  • Audio ML with Python

  Articles:

  • Whisper Paper - OpenAI
  • Azure Speech Service Docs
  • Speech-to-Text Best Practices

6.2 AI Safety & Responsible AI

• Safety Considerations

  • Prompt injection prevention
  • Content filtering
  • Jailbreak prevention
  • Output validation

  Video Tutorials:

  • AI Safety Course - Center for AI Safety
  • Prompt Injection Explained - OWASP
  • LLM Security - DeepLearning.AI
  • AI Red Teaming - Microsoft

  Articles:

  • OWASP LLM Top 10
  • Prompt Injection Guide - Simon Willison
  • Azure Content Safety

• Responsible AI

  • Bias detection and mitigation
  • Fairness metrics
  • Explainability (SHAP, LIME)
  • Privacy and data protection

  Video Tutorials:

  • Responsible AI - Microsoft
  • AI Ethics Course - fast.ai
  • SHAP Values Explained - StatQuest
  • Fairness in ML - Google

  Articles:

  • Microsoft Responsible AI
  • SHAP Documentation
  • AI Fairness 360 - IBM
  • Google What-If Tool

6.3 Emerging Technologies

The field moves fast. Really fast. You need to keep up.

• Stay Current (This is an ongoing effort)

  • Mixture of Experts (MoE) - how modern LLMs get so big efficiently
  • State Space Models (Mamba) - challenging transformers
  • Retrieval-augmented fine-tuning - combining RAG with fine-tuning
  • Agentic workflows - the next frontier

  YouTube Channels to Subscribe:

  • Mixture of Experts Explained - Yannic Kilcher
  • Mamba Explained - Umar Jamil
  • Two Minute Papers
  • AI Explained

  Blogs to Follow:

  • Lil'Log - Lilian Weng
  • The Batch - DeepLearning.AI
  • Papers With Code
  • Sebastian Raschka's Newsletter

---

Phase 7: Specialisation & Leadership (Ongoing)

At this point, you're not just learning - you're deciding what kind of AI engineer you want to be.

7.1 Choose Your Path

• AI Platform Engineering (For those who love infrastructure)

  • Building AI infrastructure at scale
  • Scalable AI systems
  • Cost optimisation (this becomes critical at scale)

• Applied AI/ML Engineering (For those who love building products)

  • Domain-specific applications
  • Custom model development
  • Taking research to production

• AI Architecture (For those who love system design)

  • System design for AI
  • Enterprise AI patterns
  • AI governance

7.2 Staff+ AI Engineer Skills

This is where your experience as a staff engineer comes in. Technical leadership in AI is about more than just coding.

• Technical Leadership

  • AI strategy and roadmap - where should your team focus?
  • Build vs buy decisions - when to use off-the-shelf vs custom
  • Vendor evaluation - OpenAI vs Azure vs Google vs Anthropic
  • Technical debt management - yes, AI systems have this too

• Cross-functional Collaboration

  • Working with data scientists - different perspectives, same goal
  • Product and AI alignment - what's actually useful?
  • Stakeholder communication - explaining AI to non-technical folks
  • Mentoring engineers - sharing what you've learnt

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Resources I Actually Use

Right, here's the honest list. Not everything - just the stuff that's genuinely helpful.

Books Worth Your Money

Title	Author	Focus Area	Link
Designing Machine Learning Systems	Chip Huyen	MLOps	O'Reilly
Natural Language Processing with Transformers	Tunstall et al.	NLP/LLMs	Hugging Face
Deep Learning	Goodfellow, Bengio, Courville	Foundations	Free Online
Hands-On Machine Learning (3rd Ed)	Aurélien Géron	Practical ML	O'Reilly
Build a Large Language Model (From Scratch)	Sebastian Raschka	LLMs	Manning

Online Courses (Highly Recommended)

Course	Platform	Duration	Link
Machine Learning Specialization	Coursera (Andrew Ng)	3 months	Coursera
Deep Learning Specialization	Coursera (deeplearning.ai)	4 months	Coursera
Generative AI with LLMs	Coursera (AWS/DeepLearning.AI)	1 month	Coursera
Fast.ai Practical Deep Learning	fast.ai	Self-paced	fast.ai FREE
Hugging Face NLP Course	Hugging Face	Self-paced	HF Course FREE
Full Stack LLM Bootcamp	FSDL	Self-paced	FSDL FREE
Azure AI Engineer Associate	Microsoft Learn	Self-paced	MS Learn

YouTube Channels I Follow Religiously

Channel	Focus	Why I Like Them
3Blue1Brown	Maths Visuals	Makes maths beautiful, genuinely
Andrej Karpathy	Neural Networks	Ex-Tesla AI Director, builds from scratch
StatQuest	ML Concepts	Josh makes complex stuff simple
Yannic Kilcher	Paper Reviews	Deep dives into research papers
AI Explained	Latest AI	Good analysis of new developments
Sam Witteveen	Applied AI	Practical implementations
DeepLearning.AI	Courses	Andrew Ng's team

Newsletters & Blogs (How I Stay Updated)

Resource	Frequency	My Take
The Batch (DeepLearning.AI)	Weekly	Curated by Andrew Ng, never miss this
Import AI (Jack Clark)	Weekly	In-depth analysis
Sebastian Raschka's Ahead of AI	Weekly	Excellent research summaries
Lil'Log (Lilian Weng)	Monthly	OpenAI researcher, comprehensive posts
AI Snake Oil	Irregular	Cuts through the hype, important perspective
Simon Willison's Blog	Frequent	Practical LLM insights

Free Short Courses That Are Actually Free (DeepLearning.AI)

These are brilliant. Each is about an hour, and they don't try to upsell you constantly.

Course	Duration	What You'll Learn
ChatGPT Prompt Engineering	1 hour	Essential prompting skills
Building with LLMs	1 hour	Practical LLM applications
LangChain for Development	1 hour	LangChain basics
Building RAG Applications	1 hour	RAG implementation
Finetuning LLMs	1 hour	When and how to fine-tune
AI Agents in LangGraph	1 hour	Building AI agents

Certifications (If Your Organisation Values Them)

Honestly, certifications matter more in some companies than others. But they do help structure your learning.

• Azure AI Engineer Associate (AI-102) - Exam Guide - Most relevant for .NET folks
• Azure Data Scientist Associate (DP-100) - Exam Guide
• AWS Machine Learning Specialty - Exam Guide - If you're in AWS land
• Google Professional ML Engineer - Exam Guide
• TensorFlow Developer Certificate - Exam Guide - Shows practical skills

Communities (Where the Conversations Happen)

• Hugging Face - Models, datasets, great community
• r/MachineLearning - Research discussions, sometimes heated debates
• r/LocalLLaMA - Running LLMs locally, very active
• Discord: Nous Research - Open source AI enthusiasts
• Discord: LangChain - LangChain help and discussions
• Twitter/X - Follow AI researchers, that's where news breaks first
• arXiv cs.CL & cs.LG - Latest papers, if you want to go deep

---

Project Ideas (Learning by Doing)

The best way to learn is to build. Here are some project ideas, roughly in order of difficulty.

Beginner Projects (Start Here)

1. Sentiment Analyser with ML.NET - Classic first project

   • Tutorial: ML.NET Sentiment Analysis
   • Video: Build ML Model in C#

2. Simple RAG Chatbot

   • Tutorial: Semantic Kernel Quickstart
   • Video: Build a RAG App

3. Image Classifier

   • Tutorial: Image Classification with ONNX

Intermediate Projects (Once You're Comfortable)

4. Multi-source RAG System

   • Article: Advanced RAG Techniques
   • Video: RAG from Scratch

5. Code Review Assistant

   • Reference: GitHub Copilot Extension Building

6. Custom Copilot for Your Domain

   • Tutorial: Build Your Own Copilot - Azure

Advanced Projects (Show Off Time)

7. Multi-Agent System

   • Tutorial: AutoGen Multi-Agent
   • Video: Build Multi-Agent Systems

8. Fine-tuned Model for Your Domain

   • Tutorial: Fine-tune with Hugging Face
   • Video: Fine-tuning Tutorial

9. Production AI Pipeline

   • Tutorial: Azure ML End-to-End
   • Course: Made With ML MLOps

---

Weekly Study Schedule (What Works for Me)

This isn't set in stone - adjust based on your life. The key is consistency over intensity.

Day	What I Do	Time
Monday	Theory & concepts - watch videos, read articles	2-3 hrs
Tuesday	Hands-on coding - follow tutorials, experiment	2-3 hrs
Wednesday	Project work - build something real	2-3 hrs
Thursday	Reading & catching up - newsletters, papers	1-2 hrs
Friday	Review - revisit what I learnt this week	1-2 hrs
Weekend	Deep dive or project sprint	3-4 hrs

Roughly 12-17 hours/week - Adjust as needed. Life happens.

---

Progress Tracking

Milestones (Realistic Targets)

• Month 1: Finish ML fundamentals, build first ML.NET model
• Month 3: Build a neural network, understand transformers
• Month 6: Deploy a RAG application, implement an AI agent
• Month 9: Complete an MLOps pipeline, contribute to an AI project at work
• Month 12: Lead an AI initiative, get a certification if needed

How to Measure Progress

• How many models have I deployed (even small ones)?
• What AI features have I shipped to users?
• Have I helped others learn this stuff?
• Am I influencing technical decisions around AI?

---

Quick Reference: .NET AI Stack

Here's how all the pieces fit together when building AI applications in .NET:

flowchart TB
    subgraph APP["Application Layer"]
        direction LR
        A1[ASP.NET Core]
        A2[Blazor]
        A3[MAUI]
        A4[Console Apps]
        A5[Azure Functions]
    end
    
    subgraph ORCH["AI Orchestration"]
        direction LR
        O1[Microsoft Semantic Kernel]
        O2[AutoGen]
        O3[LangChain .NET]
    end
    
    subgraph AI["AI Services / LLM Providers"]
        direction LR
        S1[Azure OpenAI]
        S2[OpenAI]
        S3[Anthropic Claude]
        S4[Google Gemini]
        S5[Ollama - Local]
    end
    
    subgraph VEC["Vector Stores / RAG"]
        direction LR
        V1[Azure AI Search]
        V2[Qdrant]
        V3[Chroma]
        V4[Pinecone]
        V5[FAISS]
    end
    
    subgraph ML["ML/DL Frameworks"]
        direction LR
        M1[ML.NET]
        M2[TorchSharp]
        M3[ONNX Runtime]
        M4[TensorFlow.NET]
    end
    
    APP --> ORCH
    ORCH --> AI
    ORCH --> VEC
    ORCH --> ML
    AI --> VEC
    
    style APP fill:#4a90d9,stroke:#2c5aa0,color:#fff
    style ORCH fill:#50c878,stroke:#3a9a5a,color:#fff
    style AI fill:#ff7f50,stroke:#d35f30,color:#fff
    style VEC fill:#9370db,stroke:#7350bb,color:#fff
    style ML fill:#ffd700,stroke:#ccaa00,color:#333

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How to read this:

• Application Layer - Where your code lives (web apps, APIs, desktop apps)
• AI Orchestration - The core that connects everything
• AI Services - The brains (LLMs that do the heavy thinking)
• Vector Stores - Memory for RAG (where you store embeddings)
• ML/DL Frameworks - For custom models and inference

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Last Updated: December 2025
By: Senthil Sekar