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

Learn how to augment Amazon SageMaker with open-source tools for ML, to build a robust ML Infrastructure

On-Demand Workshop

The Amazon SageMaker and Open-Source for ML is a hands-on workshop to help DevOps and Infrastructure teams, ML Engineers, MLOps professionals, and architects to learn how to mix and match fully managed AWS services and cutting-edge open-source tools for Machine Learning. The event has hands-on labs and modules on building a customizable ML infrastructure, practical cases of using Amazon SageMaker in combination with open-source components, and a live demo on ML lifecycle implementation.

Agenda:

  • Overview of Modern ML Infrastructure on AWS combined with open-source components
  • Real-world case study on building ML Infrastructure for Vision Screening with Amazon SageMaker and Kubeflow
  • Hands-on walk-through demo of the complete ML Engineer Experience — ML Lifecycle: From Data to ML Model Deployment and Retraining

Speakers:

  • Rinat Gareev, ML Solutions Architect, Provectus
  • Dmitrii Evstiukhin, Senior Solutions Architect, Provectus
  • Lenar Gabdrakhmanov, ML Engineer, Provectus

Who should attend:

  • DevOps teams
  • Infrastructure teams
  • ML Engineers
  • MLOps professionals
  • Architects

Let’s explore industry best practices of combining Amazon SageMaker with the best open-source tools and frameworks for Machine Learning!

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On-Demand Workshop

The Amazon SageMaker and Open-Source for ML is a hands-on workshop to help DevOps and Infrastructure teams, ML Engineers, MLOps professionals, and architects to learn how to mix and match fully managed AWS services and cutting-edge open-source tools for Machine Learning. The event has hands-on labs and modules on building a customizable ML infrastructure, practical cases of using Amazon SageMaker in combination with open-source components, and a live demo on ML lifecycle implementation.

Agenda:

  • Overview of Modern ML Infrastructure on AWS combined with open-source components
  • Real-world case study on building ML Infrastructure for Vision Screening with Amazon SageMaker and Kubeflow
  • Hands-on walk-through demo of the complete ML Engineer Experience — ML Lifecycle: From Data to ML Model Deployment and Retraining

Speakers:

  • Rinat Gareev, ML Solutions Architect, Provectus
  • Dmitrii Evstiukhin, Senior Solutions Architect, Provectus
  • Lenar Gabdrakhmanov, ML Engineer, Provectus

Who should attend:

  • DevOps teams
  • Infrastructure teams
  • ML Engineers
  • MLOps professionals
  • Architects

Let’s explore industry best practices of combining Amazon SageMaker with the best open-source tools and frameworks for Machine Learning!