About the Author
Chip Huyen
ISBN 9789355422675
Rs. 2,560
O'Reilly Media
Designing Machine Learning Systems
Who it's for: engineers and data scientists building production ML systems
"Designing Machine Learning Systems" by Chip Huyen offers a practical, end-to-end guide for building production-ready ML applications. This book provides an iterative approach to designing scalable, reliable, and maintainable systems, covering essential aspects from data engineering and model deployment to monitoring and continuous improvement in real-world settings.Why You Should Read? Learn a structured, iterative methodology for designing robust machine learning systems. Acquire practical techniques for handling data, deploying models, and monitoring performance in production environments. Understand and address common challenges such as model drift, bias, feedback loops, and scalability. Explore MLOps best practices to streamline ML workflows and foster effective cross-functional collaboration.
Highlights
- Iterative methodology for designing scalable, reliable ML systems
- Covers data engineering, model deployment, and monitoring
- Addresses model drift, bias, feedback loops, and scalability
- Explores MLOps best practices for streamlined workflows
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Questions about this book
What is the main focus of this book?
It provides a practical, end-to-end guide for building production-ready ML applications, with an iterative approach to designing scalable, reliable, and maintainable systems.
Does the book cover MLOps practices?
Yes, it explores MLOps best practices to streamline ML workflows and foster effective cross-functional collaboration.
What common challenges does the book address?
It addresses model drift, bias, feedback loops, and scalability, among other real-world challenges.
Who is this book intended for?
It is intended for practitioners involved in building and maintaining machine learning systems, such as engineers and data scientists.