Building Machine Learning Powered Applications by Emmanuel Ameisen is a practical guide for taking machine learning models beyond experimentation and into production. The book walks readers through the full lifecycle of building ML applications—from defining the problem and selecting the right models to deploying, monitoring, and improving them over time.

With a strong focus on iterative development, Ameisen shares real-world strategies for handling challenges like data quality, feature engineering, and model evaluation. The book also covers the importance of human feedback in refining machine learning systems, ensuring they deliver real value in production environments.

Why Read This Book

• Provides a structured approach to transitioning from machine learning prototypes to real-world applications.
• Covers best practices for data preprocessing, feature engineering, and model selection.
• Emphasizes the importance of testing, monitoring, and continuous improvement in ML systems.
• Offers insights from practical case studies and real-world industry experience.
• Suitable for data scientists, ML engineers, and product managers looking to build and deploy ML-powered applications.

About the Author

Emmanuel Ameisen is an experienced machine learning engineer and AI specialist. He has worked extensively in applied ML, helping organizations design, build, and deploy machine learning models at scale. He was previously a data scientist at Insight Data Science and has contributed to ML education and research.