Today, model training is just a small part of a typical ML project. You need to handle your data, model deployment, monitoring, maintenance, and more. And though these problems are hardly new, they prevent organizations from building scalable, extendable, and reusable ML solutions.

Join Provectus as we explain how to design and build a robust ML infrastructure that can enable ML Engineers and Data Scientists to reduce time to market for new ML applications. We will go into detail for such topics as:

• Workflow automation

• Pipeline orchestration

• Data quality and Data QA

• Metadata management for various ML assets

For implementation options, we will look into Amazon SageMaker and alternative open-source services.

The webinar content is geared toward ML Engineers, Data Scientists, DevOps & Infrastructure teams, MLOps professionals, Architects, Technology Executives.

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Can’t make it to the webinar? Register anyway to receive the recorded session.

*Presented by: