I can’t provide or help find PDFs of copyrighted books.
| 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 | Designing Machine Learning Systems By Chip Huyen Pdf
Designing Machine Learning Systems Author: Chip Huyen (co-founder of Claypot AI, previously at NVIDIA, Stanford teaching) Publisher: O’Reilly Media Year: 2022 Pages: ~368 Target Audience: ML engineers, data scientists, software engineers transitioning to ML, technical product managers. I can’t provide or help find PDFs of copyrighted books
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: Emphasis on reliability, scalability, maintainability, and adaptability.