Webinar ·Video
MLOps and Reproducible ML on AWS with Kubeflow and SageMaker
Learn how to build a secure and compliant ML infrastructure to manage the full ML lifecycle on AWS.
On-Demand Webinar
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
Speakers
- 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
- Developers
Let’s learn how to build an end-to-end infrastructure for machine learning using Kubeflow and SageMaker.
Request the Webinar