ML Engineering & MLOps Fundamentals — Machine Learning Roadmap
Treating ML work like real software: reproducible, tracked and versioned
Steps in ML Engineering & MLOps Fundamentals
- ML Project Structure & Reproducibility — advanced · Organizing code, data and configuration so results can be reproduced later
- Experiment Tracking (MLflow/Weights & Biases) — advanced · Logging parameters, metrics and artifacts across many training runs
- Model Versioning & Registries — advanced · Tracking which model version is deployed where, and rolling back safely
- Data Versioning (DVC) — advanced · Versioning large datasets alongside code the way Git versions source files
- Pipelines with scikit-learn Pipeline/ColumnTransformer — advanced · Chaining preprocessing and modeling steps into a single, reusable object
Part of
- Machine Learning roadmap — the full learning path