Review Week 1 & Build a Simple ML Project
Consolidate everything from Days 1–6 by building a fully versioned, end-to-end Iris classification project with Git, DVC, and a reproducible pipeline.
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DVC Pipelines & Experiment Tracking
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.
Data Versioning & DVC Introduction
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.
Version Control with Git for ML Projects
Stop losing ML experiments. Learn Git workflows, branching strategies, and .gitignore best practices to make your ML projects reproducible, organized, and production-ready.
Python Refresher for ML Engineering Tasks
Master functions, classes, decorators, and file I/O. Graduate from notebooks to production-grade ML scripts.
Setting Up Your MLOps Development Environment
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.