Thoughtful Machine Learning Ops

Human involvement is essential in successful ML deployments. That's why Deeploy standardizes Deployment, Monitoring and Explainability of ML models.

Which problems do we solve?

Manageable ML in production for every team

Growing data teams work together on multiple models in different versions connected to multiple clouds and services. Deeploy offers an adaptive way to collaborate on these models in different environments and let users deploy their models on a unified platform.

Screenshot of Manageable ML in production for every team

Accountable and reproducible decisions

ML in production is an ever growing part of the final decision making. That’s why - just like every decision made by humans - a decision made by a model has to be traceable and accountable, such that people can verify and reproduce the outcomes.

Screenshot of Accountable and reproducible decisions

Explainable and transparent for everyone

ML models become more complicated since new models are built on the shoulders of existing ones. ML engineers, data scientists, content experts and business people need to understand the way of working of a model in order to keep control. All in their own way and with their own focus.

Screenshot of Explainable and transparent for everyone

The team

Photo of Tim Kleinloog

Tim Kleinloog

Technical Director / Co-Founder

Photo of Bastiaan van de Rakt

Bastiaan van de Rakt

Commercial Director / Co-Founder

Photo of Bob van de Helm

Bob van de Helm

Software Engineer

Photo of Lars Suanet

Lars Suanet

Software Engineer

Photo of Koen van Marrewijk

Koen van Marrewijk

Software Engineer

Photo of Ivar Sanders

Ivar Sanders

Intern Software Engineer

Photo of Maarten Stolk

Maarten Stolk


Photo of Nick Jetten

Nick Jetten


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