As machine learning (ML) becomes a critical driver of innovation, organizations are increasingly looking for robust, scalable environments to support data scientists in their experimentation, model training, and deployment workflows. AWS offers a comprehensive suite of tools and services, led by Amazon SageMaker, to build such environments while addressing challenges

Data lakes have become essential for organizations seeking to store vast amounts of structured and unstructured data. However, managing these repositories presents several challenges: * Data Consistency and Integrity: Traditional data lakes often lack support for ACID (Atomicity, Consistency, Isolation, Durability) transactions, leading to issues with data consistency, especially during concurrent