Google Cloud Platform: MLOps

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 organizations is not on its own.
The challenge of...


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


This solution makes use of


Google Cloud Platform

With Google Cloud you make custom-made solutions and integrates your current systems in a scalable manner.

Curious to know how we can help your organization?

Reach out to our account manager Toon

Toon Pellemans

Meet the Infrastructure Squad


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