Since it was released, Amazon SageMaker has shown it can drastically reduce a data scientist's time and cost of implementing enterprise ML projects into production systems. Over the past year, SageMaker has released other features to enhance the performance and capabilities of this service for a variety of ML use cases.

Features such as SageMaker Experiments, AutoPilot, and Ground Truth offer a quick and straightforward way to get reasonable models developed, trained, and deployed. Other features such as Script Mode, Pipe Mode, and Inference Pipelines give the data scientist more flexibility to fine-tune their ML process. These recent changes to SageMaker accommodate both the newer data scientist who might be starting their first ML project and the experienced one who is looking to optimize their workflow further. With all of these new features and capabilities, no time is better to start using SageMaker than right now.

In this webinar, we will:

• Give you a quick high-level overview of the functionality and capabilities of SageMaker

• Introduce important new features such as Script Mode, AutoPilot, Experiments etc.

• Go through a SageMaker demo that shows how these features can be used together in a ML ecommerce business scenario

• Give some useful tips to get the most out of these new features