These notes were originally inspired by Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, and Michael Munn, but have evolved and expanded significantly over time with additional insights, examples, and patterns drawn from practical experience.
These notes are designed for data scientists and engineers with foundational ML knowledge who want to focus on practical applications rather than theoretical depth.
To create a concise, living reference that is useful to practitioners and learners alike—capturing proven patterns and best practices for real-world ML engineering challenges.
Many ML applications face recurring challenges, which is why ML design patterns are so valuable—they provide proven solutions and best practices for commonly encountered problems. These notes serve as a catalog of such patterns in ML engineering.
These notes were originally compiled for my own study, and I hope they provide value to others.
| Chapter | What it covers |
|---|---|
| From Raw Data to Features | Scaling, encoding, embeddings, feature crosses, multimodal inputs |
| Problem Formulation Patterns | Reframing, multilabel, cascading, neutral class, rebalancing |
| Training Optimization Patterns | Overfitting tricks, checkpoints, transfer learning, ensembles, distributed training |
| Reproducibility Patterns | Transform parity, repeatable splits, bridged schemas |
| Production Serving Patterns | Stateless serving, batch inference, two-phase predictions, feature stores |
| MLOps Patterns | Workflow pipelines, versioning, continuous evaluation |
| Responsible AI | Explainability (SHAP, IG, counterfactuals), fairness & bias detection |
| Topic | Notes | Status |
|---|---|---|
| 📊 Model Evaluation & Metrics | Evaluation metrics appear scattered across Problem Formulation (rebalancing) and MLOps (continued evaluation), but there's no systematic coverage of choosing metrics, handling imbalanced evaluation, or offline vs online metrics. | ✅ Will add |
| 🧪 Experiment Tracking & A/B Testing | Model versioning is covered for deployment, but no coverage of experiment tracking (MLflow, Weights & Biases) or online experimentation. | ✅ Will add to reproducibility section |
| ❌ Data Validation & Quality | Intentionally kept this guide focused on ML rather than Data Engineering. | 🚫 Will not add |