Oreilly – Machine Learning System Design, Video Edition 2025-2

Oreilly – Machine Learning System Design, Video Edition 2025-2

19 Oct 2025

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Description

Machine Learning System Design, Video Edition. This course provides a comprehensive, practical, step-by-step guide to designing effective, reliable, and scalable machine learning systems. It provides a complete and reliable framework for building, maintaining, and enhancing machine learning systems of any level of complexity. This course guides you through all stages of the project lifecycle, from data collection to deploying and maintaining the system in a production environment. For any professional, whether an engineer adding machine learning capabilities to an existing application or someone looking to build a system from scratch, managing large datasets, defining testing and deployment requirements, and navigating the unique complexities of running models in production is essential. This course is designed to address these challenges precisely, showing you how to design and deploy a machine learning project from start to finish. Authors Valery Babushkin and Arseny Kravchenko have enriched this unique guide with their personal insights and extensive practical experience. Participants benefit directly from their expertise and learn hands-on about every aspect of the system, including requirements gathering, data provisioning, deployment, and management of the final system.

What you will learn

Overview and big picture of machine learning system design.
Problem space analysis to identify the optimal machine learning solution.
Prepare for machine learning system design interviews (Ace ML system design interviews).
Selecting appropriate metrics and evaluation criteria.
Prioritizing tasks at different stages of machine learning system design.
Solve data set problems with data collection, error analysis, and feature engineering.
Identify common pitfalls in machine learning system development.
Designing machine learning systems to be lean, maintainable, and extensible.
Metrics and evaluation criteria.
Solve common dataset problems.
Common pitfalls in ML system development.
Machine learning system design interview tips.

This course is suitable for people who:

For readers who know the principles of Software Engineering and Machine Learning.
Engineers and technical professionals looking to add machine learning capabilities to existing applications or design machine learning systems from the ground up.
People preparing for specialized interviews in machine learning systems design.