Junior engineers always ask me: "What tools should I learn for MLOps?"

And I always give the same frustrating answer: "It depends."

But let me at least give you the framework. Every production ML system needs something from each of these categories:

Experiment tracking: MLflow or Weights & Biases. Data versioning: DVC. Feature store: Feast. Model serving: FastAPI + Docker (start here) or Triton for scale. Monitoring: Evidently AI is great open source. Orchestration: Airflow or Prefect. CI/CD: GitHub Actions.

You don't need all of these on day one. Start with experiment tracking (MLflow) and containerized serving (FastAPI + Docker). Add monitoring (Evidently). Then layer in data versioning and orchestration as your system matures.

The mistake I see: teams trying to implement the full stack from the start. That's a six-month project before you've deployed a single model.

The right approach: ship a simple pipeline first. Add sophistication as you feel the pain of not having it. That pain guides your tooling choices better than any blog post.