Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Amazon SageMaker Studio: Build production-grade machine learning models serves as a comprehensive guide for data scientists and machine learning engineers aiming to master the first cloud-based integrated development environment (IDE) for machine learning. The book details how to navigate the entire ML lifecycle within the Amazon SageMaker Studio ecosystem, from initial data preparation to final model deployment. Readers are introduced to the platform's user interface and core features, learning to integrate essential workflows such as feature engineering, statistical bias detection, and automated machine learning (AutoML) into a unified environment. The text progresses through practical applications, demonstrating how to build datasets, host feature stores, and train models with scalability in mind. It covers advanced topics like detecting bias with SageMaker Clarify, utilizing low-code solutions via SageMaker JumpStart and Autopilot, and ensuring optimal performance through rigorous model monitoring. By addressing common production challenges, the book provides actionable strategies for operationalizing machine learning projects using pipelines and model registries, ultimately helping professionals improve productivity and governance in their ML development processes. This resource is designed for those with basic knowledge of data science who wish to gain hands-on experience with cloud-based ML operations without requiring prior SageMaker expertise. By the end of the guide, readers will understand how to effectively scale the ML lifecycle, apply best practices for cloud resource management, and deploy robust models for diverse use cases. The content ensures a thorough understanding of how to leverage Amazon SageMaker Studio to streamline development and achieve operational excellence in machine learning projects.