🎯 AI Architect @ Microsoft | 📚 Published Author | 🔬 Independent Researcher
"Bridging the gap between cutting-edge AI research and real-world applications through innovative cloud-native solutions"
I'm passionate about building the next generation of intelligent systems that transform industries. My expertise spans Large Language Models, Deep Reinforcement Learning, and Distributed ML Systems. I architect scalable AI solutions on the cloud while contributing to the AI community through research, writing, and open-source projects.
- 🏗️ Architect AI Solutions at scale for enterprise applications
- 📝 Write Technical Content on LinkedIn and Medium
- 🔬 Research in Deep Reinforcement Learning and Industrial Automation
- 🌟 Mentor developers building intelligent applications
Sharing knowledge through comprehensive guides on AI and cloud technologies
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📫 How to reach me: Write to vishwanath.srikanth@mail.com / ping me on linked-in
👯 I’m looking to collaborate on research work related to reinforcement learning.
⚡ Fun fact: I'm actually not as busy as it appears :)
sriksmachi/sriksmachi is a ✨ AI/ML ✨ repository where you can find all my work.
Here are some ideas to get you started:
- 🔭 I’m currently working on applied RL
- 🌱 I’m currently learning distributed ML systems
- 👯 I’m looking to collaborate on RL in the field of industrial automation
- 🤔 I’m looking for help with ...
- 💬 Ask me about ML/DS/AI, designing distributed systems for the cloud, microservices design
"Where Applied Research solves real-business problems"
The sriksmachi repository is a ✨ comprehensive AI/ML knowledge hub ✨ showcasing production-ready solutions, research implementations, and educational resources. Each section below contains battle-tested examples, interactive notebooks, and mini-projects applicable across industries.
Real-world applications showcasing AI innovation in action
| 🚀 Project | 🔗 Repository | 💡 Innovation |
|---|---|---|
| 🤖 Multi-Agent AI System | View Project → | Language acceleration for multi-agent systems |
| 🚕 SuperCabs | View Project → | RL/Q-Learning-based decision framework for car-rental services like uber, that maximises profit |
| 🏢 RBEI | View Project → | YOLO-based household object detection for edge devices & smart cleaning robots |
| 🔷 Azgentica | View Project → | Vision-powered AI agent transforming Azure architecture diagrams into structured insights & cost analysis |
Reinforcement Learning & Distributed ML Systems
- Exploring advanced techniques in RL applications for industrial automation [supply chain orders] and intelligent systems [RL-based decision system for AI trading with market sentiment analysis]
- Focusing on distributed training and large-scale model optimization
- Active experimentation with multi-agent systems and language model acceleration
Research Interests:
- 🤖 Deep Reinforcement Learning applications in robotics and automation
- 🔄 Distributed training for large-scale AI systems
- 🤝 Multi-agent AI systems and coordination
- ⚡ Language model optimization and acceleration techniques
- ☁️ Cloud-native distributed ML architectures
How to Engage:
- 💬 Interested in collaborating on RL research? Reach out via LinkedIn
- 📝 Follow my research explorations on Medium
- 🔗 Explore my active research repositories above
The following links point you to AI ML topics that that can be learnt in 30 minutues with code and examples.
| 🔥 Domain | 🚀 Repository | 📊 Focus Area |
|---|---|---|
| 🌐 Azure ML | Explore → | Cloud-native ML solutions |
| 🧠 Large Language Models | Explore → | LLM applications & fine-tuning |
| 📈 Classical Machine Learning | Explore → | Traditional ML algorithms & Concepts |
| 🎮 Reinforcement Learning | Explore → | Reinforcement learning concepts & applications |
