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.
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...
