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
MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·May 18 · 12 min readBuilding Bulletproof ML Pipelines: Feature Engineering, Scaling, and Leakage-Free Preprocessing in scikit-learnWhy pipelines matter more than your algorithm choice There's a humbling pattern every ML engineer eventually faces: you train a model with 94% accuracy, deploy it, and watch it collapse to 61% in prod00
MMMungara Muddu Krishna Yadavinmuddukrishna.hashnode.dev·May 12 · 12 min readOne Million Jobs: The 2026 AI Engineer & Gen AI Developer RoadmapFrom zero to production-ready — a practical, phase-by-phase path to one of the most in-demand careers of our time. 👋 Why I Wrote This AI engineering is no longer a niche specialisation. In 2026, it10
