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.
Stop running ML code in isolation. MLflow Projects make your experiments reproducible on any machine — while the Model Registry gives your models a professional lifecycle: from experiment to staging to production. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 9)
Stop losing track of your best model. Learn how MLflow turns chaotic trial-and-error into a reproducible, queryable history of every experiment you've ever run. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 8)
Consolidate everything from Days 1–6 by building a fully versioned, end-to-end Iris classification project with Git, DVC, and a reproducible pipeline. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 7)
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)
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
