AI/ML projects are highly dependent on data. A large volume of data needs proper infrastructure to run the data processing, ML training & tuning jobs. Amazon Sagemaker brings a broad set of capabilities required for end-to-end ML lifecycle implementation. Now Data scientists & developers can focus on innovation, research, Model development without any infrastructure hassle.

In this session learn how Sagemaker MLOps can support your data science projects from POC to production through deployment to maintenance. This session will focus on a hands-on project to build, train and deploy a cost-effective, scalable Machine Learning Model using Amazon Sagemaker.

Technical session | Duration -1 hr. | Level - Intermediate| Ideal for students, professionals, startup owners who want to build scalable production-ready AI/Ml system