A trained model sitting in a .pkl file helps nobody. Today your iris classifier gets a front door: a REST API that any application, in any language, can call over HTTP. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 15)
Stop running scripts manually. Learn how to define reproducible, dependency-aware ML pipelines using DVC's declarative dvc.yaml, track every run automatically, and share exact pipeline states with your team — no Makefile required. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 6)
Why your ML experiments are irreproducible without proper data versioning and how DVC pairs with Git to give every dataset, model, and artifact a permanent, auditable address. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 5)
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)
90 Days of Cloud Challenge: Day 28
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
