Any machine learning project is more than just building and deploying models. It is about the complete lifecycle involving ML, DevOps, and data engineering that is built on top of and supported by ML infrastructure.
Join Provectus & AWS as we explain how to build a robust ML infrastructure on AWS and show why MLOps and reproducible ML are key to enabling the delivery of ML-driven innovation at scale.
You will learn about:
Reusable Feature Store with reproducible data preparation pipelines
Reproducible experimentation & model training pipelines
Continuous Integration and Delivery for ML (MLOps)
Production monitoring and model re-training
Stepan Pushkarev, Chief Technology Officer, Provectus
Alex Chung, Sr. Product Manager in Deep Learning, AWS
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 learn how to build an end-to-end infrastructure for machine learning using Kubeflow and SageMaker!