Milestone 6: MLOps & Deployment

⚙️ Milestone 6: MLOps & Deployment

Turn your models into production software. Track experiments, version datasets, and automate deployments.

📚 Slow-Paced Deep Dives (University Modules)

• Module 1: Experiment Tracking (The Lab Notebook): OPS-601. Why we need a “Logbook” for every model run.
• Module 2: Model & Data Versioning (The Time Machine): OPS-602. DVC and how to save “State.”
• Module 3: Containerization & Docker (The Shipping Container): OPS-603. Packaging your model for everywhere.
• Module 4: API Serving & FastAPI (The Waiter): OPS-604. Serving models as high-performance services.

🚀 The Senior’s Perspective

• Advanced MLOps: MLflow deeper dive, Observability, and CI/CD pipelines.

🥅 Milestone Goals

1. Log hyperparameters and metrics using MLflow.
2. Save 100GB datasets using DVC library cards.
3. Wrap models in Docker Containers for portability.
4. Build a high-performance REST API using FastAPI.

:::tip Final Senior Tip Deployment is not the end. A Senior monitors models for “Drift” (when the real world changes and your model gets stupid). Use Milestone 8 for that! :::