| Chapter | Title | Key Concepts | |---------|-------|----------------| | 1 | Overview of ML Systems | ML vs software, when to use ML, iterative process | | 2 | Data Engineering | Sources, formats, schema evolution, data lineage | | 3 | Feature Engineering | Feature extraction, transformation, feature stores | | 4 | Model Training & Tuning | Experiment tracking, hyperparameter tuning, scaling training | | 5 | Model Evaluation | Offline vs online metrics, bias/fairness, A/B testing pitfalls | | 6 | Model Deployment | Batch vs real-time, canary releases, blue-green deployment | | 7 | Monitoring & Observability | Data drift, concept drift, alerting, dashboards | | 8 | Continuous Integration & Delivery (CI/CD) for ML | Pipelines, testing data/model/code, MLOps | | 9 | Infrastructure & Scaling | Cloud vs edge, GPU management, orchestration (Kubernetes) | | 10 | Human Side of ML Systems | Team structures, ethics, documentation, reproducibility |
This is where the book distinguishes itself from standard theory texts. It covers the complexities of deployment strategies—batch prediction versus online prediction, the trade-offs between cloud and edge computing, and the infrastructure required to serve models at scale. Designing Machine Learning Systems By Chip Huyen Pdf
A deep dive into the "plumbing" of AI—choosing between batch vs. stream processing, managed services vs. custom builds, and the role of feature stores. | Chapter | Title | Key Concepts |
If you are searching for , you are likely looking for a roadmap to navigate the complex journey of bringing machine learning models from a notebook to a reliable, scalable production environment. stream processing, managed services vs