As data science teams scale their machine learning (ML) initiatives, the need for robust, automated, and production-ready MLOps infrastructure becomes critical. However, many data scientists lack the cloud and DevOps expertise required to build and manage scalable ML pipelines effectively. This talk explores how to empower data science teams to design and deploy MLOps infrastructure on AWS using best practices and cloud-native services.

We will discuss key AWS services such as AWS Service Catalog, Sagemaker AI, SageMaker Pipelines, Sagemaker Project, Code Pipeline, Code Build, and infrastructure-as-code (IaC) tools like Cloud Formation. Attendees will learn how to set up automated CI/CD pipelines for your ML workflow. Real-world examples will illustrate how teams can transition from ad-hoc ML workflows to fully operationalized ML systems with minimal friction. By the end of this session, data science teams and MLOps practitioners will have a clear roadmap for building maintainable and cost-effective MLOps infrastructure on AWS.