Sumit Thakur

Perfect for both newcomers and experienced practitioners looking to accelerate their AI agent development journey, this sessions will teach you to build and deploy AI agents using Amazon SageMaker Studio and Amazon Bedrock AgentCore.

Unlock the power of Amazon SageMaker Studio, a comprehensive IDE for streamlining the machine learning (ML) lifecycle. Explore data exploration, transformation, automated feature engineering with AutoML, and collaborative coding using integrated Jupyter Notebooks.

Amazon SageMaker Studio offers comprehensive tools for end-to-end ML development—from preparing data to training models, tracking experiments, deploying models, and managing pipelines—all in an integrated development environment.

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.

Jupyter notebooks allow data scientists to create, share, and streamline work for data visualization from raw code to fully functional machine learning (ML) models. With Amazon SageMaker Studio, you can spin up Jupyter notebooks quickly without the need to manage the underlying compute resources.

Machine learning (ML) is a complex, iterative, often time-consuming process. One difficult aspect is the lack of integration between the workflow steps and the tools to accomplish them.