Amazon SageMaker and Open-Source Tools for ML: Better Together

Learn how to reinforce Amazon SageMaker with open-source tools, to build a robust, highly customizable ML Infrastructure

October 7 | 11 AM PT | 2 PM ET

Many organizations rely on open-source tools to support the Machine Learning lifecycle. Amazon SageMaker has been rapidly evolving by introducing support and compatibility for various open-source frameworks. In this session, you will learn how to build a customizable ML Infrastructure based on Amazon SageMaker and open-source components. We will discuss pros and cons, the limitations of different tools that support specific stages of the ML workflow, and best practices for MLOps, to automate these stages into repeatable pipelines.

Why Attend?

  • Explore the specifics of designing and building a modern ML infrastructure on AWS
  • Get an overview of open-source tools that can be used in a bundle with Amazon SageMaker for Machine Learning on AWS
  • Learn how businesses develop ML infrastructures using SageMaker and Kubeflow, to implement AI solutions
  • Look into the ML lifecycle to learn how ML engineers handle ML models and AI solutions in the real world
  • Learn about a unique program to help businesses kick off and accelerate their ML infrastructure initiatives

Reserve Your Seat

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Reserve Your Seat

  • Hidden
  • Hidden
  • Hidden
  • Hidden
  • Hidden
  • This field is for validation purposes and should be left unchanged.

See the Provectus privacy policy for details on how we collect, use, and share information about you.

October 7 | 11 AM PT | 2 PM ET

Many organizations rely on open-source tools to support the Machine Learning lifecycle. Amazon SageMaker has been rapidly evolving by introducing support and compatibility for various open-source frameworks. In this session, you will learn how to build a customizable ML Infrastructure based on Amazon SageMaker and open-source components. We will discuss pros and cons, the limitations of different tools that support specific stages of the ML workflow, and best practices for MLOps, to automate these stages into repeatable pipelines.

Why Attend?

  • Explore the specifics of designing and building a modern ML infrastructure on AWS
  • Get an overview of open-source tools that can be used in a bundle with Amazon SageMaker for Machine Learning on AWS
  • Learn how businesses develop ML infrastructures using SageMaker and Kubeflow, to implement AI solutions
  • Look into the ML lifecycle to learn how ML engineers handle ML models and AI solutions in the real world
  • Learn about a unique program to help businesses kick off and accelerate their ML infrastructure initiatives

Who Should Attend

DevOps & Infrastructure teams, ML Engineers, MLOps professionals, Architects

Agenda

  1. Overview of Modern ML Infrastructure on AWS combined with open-source components
  2. Real-world case study on building ML Infrastructure for Vision Screening with Amazon SageMaker and Kubeflow
  3. Hands-on walk-through demo of the complete ML Engineer Experience — ML Lifecycle: From Data to ML Model Deployment and Retraining
  4. Getting started with Provectus: ML Infrastructure Acceleration Program

Ready to learn how to mix and match fully managed AWS services and cutting-edge open-source tools? Register now!