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Designing Machine Learning Systems
An Iterative Process for Production-Ready Applications
This book is 98% completeLast updated on 2026-08-02
Learn how to design, build, and deploy reliable, scalable, and maintainable Machine Learning systems in production through an iterative end-to-end process.
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About
About the Book
Machine learning systems in production are complex and require a holistic approach beyond just training models. This book provides a practical, end-to-end framework for designing ML systems that are reliable, scalable, maintainable, and adaptable to changing environments.
Key topics covered include:
- Understanding the difference between ML in research and ML in production.
- Data engineering fundamentals: storage engines, data formats, and ingestion pipelines.
- Training data curation: sampling, labeling, handling class imbalance, and data augmentation.
- Feature engineering techniques and avoiding data leakage.
- Model selection, offline evaluation, experiment tracking, and versioning.
Author
About the Author
Mohammed Akram
AI & Software Engineering Student passionate about Machine Learning systems and software development.
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