This roadmap takes you from zero MLOps knowledge to a production-ready ML engineer capable of building, deploying, monitoring, and automating end-to-end machine learning pipelines. You will master tools like MLflow, Docker, Kubernetes, Kubeflow, and cloud ML platforms while applying best practices in CI/CD, model governance, and observability. By Day 100, you will be able to design and operate scalable, reliable ML and LLM systems in real-world environments.
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)
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)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
