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AI in Production: Scaling, Monitoring, and Cost Control
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This book teaches how to move AI systems from prototypes to reliable production services. It covers packaging, inference patterns, scaling, observability, evaluation loops, cost control, and day-two operations.
Level: advancedLanguage: EN EnglishRating: 0Reviews: 0
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01Chapter 1: What Production Means for AI Systems02Chapter 2: Packaging Models, Prompts, and Artifacts03Chapter 3: Online, Batch, and Streaming Inference04Chapter 4: APIs, Queues, and Workers for AI Workloads05Chapter 5: Autoscaling Compute and GPU Planning06Chapter 6: Batching, Caching, and Concurrency Control07Chapter 7: Latency SLOs and Graceful Degradation08Chapter 8: Reliability, Retries, and Fallback Models09Chapter 9: Observability for AI Systems10Chapter 10: Quality Monitoring and Evaluation Loops11Chapter 11: Drift, Feedback, and Dataset Refresh12Chapter 12: Canary Releases, A/B Tests, and Rollbacks13Chapter 13: Cost Control for AI Workloads14Chapter 14: Containers, Kubernetes, and Serverless Inference15Chapter 15: CI/CD and Model Delivery Automation16Chapter 16: Capstone: Operating a Production AI Service17Appendix A: Commands and Workflow Cheat Sheet18Appendix B: Reference Architectures and Patterns19Appendix C: Common Mistakes and Fixes20Appendix D: Best Practices Summary
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