AI Engineering & Real-World Deployment

AI Engineering & Real-World Deployment: A Comprehensive Guide to MLOps is a definitive resource for intermediate to advanced machine learning engineers, data scientists, and DevOps professionals. While theoretical machine learning focuses on model accuracy, this book bridges the gap to production, exploring how ML systems are built, deployed, monitored, and scaled in real-world products.
Spanning 19 detailed chapters, the book covers the entire MLOps lifecycle-from setting up robust development environments and data engineering pipelines to advanced deployment strategies like canary releases and shadow mode. Readers will find practical, code-heavy implementations using industry-standard tools like Docker, Kubernetes, MLflow, and DVC . The guide also dives deep into the ethical imperatives of AI, covering fairness, explainability, and security, while providing extensive case studies in recommendation systems, fraud detection, and sentiment analysis.

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