Germany’s ZenML raises €3.4M to empower data teams with effective MLOps solutions

Munich-based ZenML, an extensible, open-source Machine Learning Operations framework for creating portable, production-ready MLOps pipelines, announced that it has secured an additional $3.7M (approximately €3.49M) in funding, bringing its total Seed funding to $6.4M.

The extension round was led by Berlin-based Point Nine with follow-on funding from ZenML’s existing investor London-based Crane Venture Partners.

The round also saw funding from D. Sculley, CEO of Kaggle; Harold Giménez, SVP R&D at Hashicorp; and Luke de Oliveira, ex-director of Machine Learning at Twilio.

Solving the real MLOps headaches

Founded in 2021 by Adam Probst and Hamza Tahir, ZenML is an open-source MLOps framework designed for collaboration between data scientists, ML engineers, and MLOps developers. It facilitates the creation of portable, production-ready MLOps pipelines. 

ZenML is suitable for various ML workflows. It is cloud- and tool-agnostic, and offers interfaces and abstractions tailored to ML processes. The framework consolidates diverse tools into a unified platform, allowing users to customise their workflows according to specific requirements.

Hamza Tahir and Adam Probst say, “We’re in this game to streamline your MLOps life, giving you more time to focus on what really matters: creating and providing the foundation for you to create and deploy valuable and useful machine learning models.”

“Being named one of Germany’s 20 most promising AI startups and the exponential growth we’ve seen in our usage has validated the approach that we’re taking to how organisations put their models into production,” they add.

What this funding means

ZenML says it will use the funds to propel its platform forward. The company is also launching ZenML Cloud, a managed service tailored for ML teams. 

ZenML Cloud includes features like Single Sign-on, Role-Based Access Control, and CI/CD integrations while preserving its open-source foundation.

“We’re currently working on some big updates to how models and data are handled throughout the full ML lifecycle that you’ll get to use soon!,” adds the company.