Companies are looking to scale and become more productive when it comes to AI and data initiatives. They seek to launch AI projects more rapidly, which, among many other factors, requires a robust machine learning infrastructure.
Join Provectus & AWS as we discuss an end-to-end architecture for ML infrastructure using Amazon SageMaker services, open source components, and a data processing and data storage ecosystem.
You will learn how to:
Create a canonical SageMaker workflow
Expand the SageMaker workflow to a holistic implementation
Enhance and expand the implementation using best practices for feature store, data versioning, ML pipeline orchestration, and model monitoring
Stepan Pushkarev, Chief Technology Officer, Provectus
Pritpal Sahota, Technical Account Manager, Provectus
Christopher A. Burns, Sr. AI/ML Solution Architect, AWS
Who should attend:
Technology executives & decision makers
Manager-level tech roles
Data engineers & Data scientists
ML practitioners & ML engineers
Let’s explore practical ways to build a robust ML infrastructure using AWS AI stack!