Making
Machine Learning
Explainable

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Success stories

Since AI is integrated in multiple of our design and production processes, it is crucial that we can rely on a robust ML operations service. The deployment functionality of Deeploy enables our data scientists to quickly iterate on their productionized models. Moreover, the fact that we can always check the reasoning behind a certain prediction, allows our continuous improvement manager to give feedback on the AI system.

Marcel Artz

CIO, Vlisco

Deeploy has been an invaluable tool in Wunderflats’ development of its production ML infrastructure. The product has provided not only reliable and performant serving of our models but also transparency in regards to explainable ML, critical for aligning business stakeholders. We are excited to see the growth of this product in the coming months.

Alex Truesdale

ML Engineer, Wunderflats

With Deeploy it's very easy to put a model into production without that much knowledge of DevOps. The clear and simple interface is very user-friendly and requires little explanation. With a few simple clicks the model is deployed and an endpoint is available for making requests to the model. The reporting section with nice graphs are also useful and thanks to the explainers you can more easy understand the outcome of your models.

Mario Wester

CTO, Yource

Why Deeploy

Deeploy creates software for Thoughtful Machine Learning Ops. With our software ML deployments are manageable, accountable and explainable by design.

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.

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.

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.

About us

lars and ivar sitting with laptops and laughing

Ensuring human involvement in Machine Learning

Over the years, we experienced the importance of human involvement with Machine Learning. Only when machine learning systems are explainable and accountable, experts and consumers can provide feedback to these systems, overrule decisions when necessary and grow their trust. That’s why we created Deeploy. 

Our story