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Machine Learning System Design

"Machine Learning: Designing Systems for Practical Use" by Chip Huyen is a comprehensive guide for professionals looking to master the intricacies of machine learning system design. This book delves into the complexities of machine learning systems, offering a holistic approach to building reliable, scalable, and sustainable systems that adapt to changing environments and business requirements. Key features include:

• Comprehensive Coverage: From data preprocessing to model evaluation and deployment, the book covers all aspects of machine learning system design.
• Real-World Examples: With real-world case studies and practical examples, readers gain hands-on experience in building effective machine learning systems.

Product Details

Written by Chip Huyen, the founder of Claypot AI, this book provides a step-by-step guide to designing machine learning systems. It covers essential topics such as data engineering, feature engineering, model development, and MLOps (Machine Learning Operations). Key topics include:

• Data Engineering: Learn how to handle data, including data sources, formats, and ETL (Extract, Transform, Load) processes.
• Feature Engineering: Explore techniques for creating meaningful features from raw data, including normalization, discretization, and encoding.
• Model Development: Understand the process of building and training machine learning models, including evaluation and optimization techniques.
• MLOps: Learn how to deploy and maintain machine learning systems in production environments.

Practical Applications

"Machine Learning: Designing Systems for Practical Use" is ideal for professionals in various fields, including data scientists, machine learning engineers, and business analysts. It provides practical solutions to real-world problems, such as:

• Engineering Data and Metrics: Learn how to select the right metrics to measure the success of your machine learning models.
• Automating Machine Learning Processes: Discover how to automate the development, evaluation, implementation, and updating of machine learning models.
• Building Monitoring Systems: Understand how to develop systems for monitoring and quickly resolving issues that may arise in production.
• Creating Responsible AI Systems: Learn about the ethical considerations and best practices for building responsible and transparent machine learning systems.