Ritesh Shah

Enterprises that own their AI lifecycle across training and inference can adapt their models as fast as their users' need change. Vanguard outgrew off the shelf models and built infrastructure that runs custom large language models (LLMs) and specialized small language models (SLMs) side by side.

A data governance framework empowers organizations to protect, curate, and understand their data throughout its lifecycle. Join this chalk talk to learn how to implement a data and machine learning governance strategy with AWS.

Amazon SageMaker offers fully managed SageMaker Studio notebooks and notebook instances for data exploration and building ML models. This session introduces next-generation SageMaker notebooks to help ML practitioners scale end-to-end ML development more efficiently.

Implementing MLOps practices helps data scientists and operations engineers collaborate to prepare, build, train, deploy, and manage models at scale. During this session, explore the breadth of MLOps features in Amazon SageMaker that help you provision consistent model development environments, automate ML workflows, implement CI/CD pipelines for ML, monitor models in production, and...