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Google Cloud Platform: MLOps

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How to operationalize your Machine Learning (ML) workflows to bring your Machine Learning models as efficient and scalable as possible into production

Bringing your ML workflows into production involves more than just having your model training and serving infrastructure in place. As an extension of DevOps best practices, MLOps includes not only CI/CD, but also continuous training (CT) and monitoring of data and model quality.

We will help you get up and running with state-of-the-art infrastructure and tools for productionizing ML workflows through workshops and close collaboration on a concrete use case.

The advantages of this programme

This program makes sure that all loose ends are connected with the use of ML, so you take advantage of them.
Build a MLOps workflow that automatically trains, validates and deploys your ML models.
Complete support and training, so your organization is not on its own.

The challenge of...

Orbisk

How can you operationalize Machine Learning workflows to deploy Machine Learning models as efficiently and scalable as possible?

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