Stop losing ML experiments. Learn Git workflows, branching strategies, and .gitignore best practices to make your ML projects reproducible, organized, and production-ready. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 4)
Master functions, classes, decorators, and file I/O. Graduate from notebooks to production-grade ML scripts. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 3)
Before pipelines, registries, and Kubernetes, every MLOps engineer needs a clean, reproducible local setup. Today you'll install Python 3.10+, master pip and virtual environments, and configure VS Code as a full ML workbench. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 2)
Machine learning models are only valuable in production. MLOps is the discipline that gets them there — and keeps them working. Let's build the foundation. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 1)
Real-world learning, production-grade projects, and AI insights for modern DevOps engineers
Model versioning, prompt versioning, evaluation pipelines — the new ops discipline for AI (Day 21)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
