MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·4d ago · 8 min readHello (AI) World: Building Your First Stateful LLM Chatbot1. Introduction: The GenAI "Hello World" In Part 5, "The Neural Bridge", we went under the hood of deep learning — activations, embeddings, the math that makes a model understand meaning. That was the00
MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·Aug 25 · 17 min readThe Neural Bridge: A Pragmatic Deep Learning Crash Course for AI Engineers1. Introduction & The Paradigm Shift If you've been following this series, you've already shipped something real. In Part 2 you learned to build ML pipelines that don't leak data. In Part 3 you picked00
MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·Jun 15 · 17 min readShipping the Brain: Packaging and Serving ML Models with FastAPI and DockerYou trained a model. It works beautifully in your notebook. Now what? Here's how to transform a Jupyter artifact into a hardened, production-grade inference microservice. The Notebook Paradigm Wall Y02M
MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·Jun 2 · 14 min readThe Algorithm Arena: A Practical Guide to Choosing and Tuning ML ModelsSo you're a software engineer stepping into the ML world. You know how to ship code. You understand systems. But now you're staring at a list of algorithms — Logistic Regression, Random Forest, XGBoos10
MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·May 19 · 13 min readBuilding Bulletproof ML Pipelines: Feature Engineering, Scaling & Leakage-Free PreprocessingPart 2 of the ML Engineering series — where clean data meets production-ready code. Why pipelines matter more than your algorithm choice There's a humbling pattern every ML engineer eventually faces:10
