Navigating Machine Learning Optimization

This chapter explores the strategic decisions involved in building a machine learning platform, particularly the buy versus build debate. It highlights the importance of automation in model tuning and the need for effective experimentation platforms while addressing the challenges of MLOps, such as model versioning. The discussion emphasizes tailored solutions in model optimization, illustrated through practical examples like using SIGOPT with complex datasets.