ZenML offers the capability to build end-to-end ML workflows that seamlessly integrate with various components of the ML stack, such as different providers, data stores, and orchestrators. This enables teams to accelerate their time to market by bridging the gap between data scientists and engineers, while ensuring consistent implementation regardless of the underlying technology.
SVP R&D at HashiCorp
"Many, many teams still struggle with managing models, datasets, code, and monitoring as they deploy ML models into production. ZenML provides a solid toolkit for making that easy in the Python ML world"
Professor of Linguistics and CS at Stanford
"ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibitiliy, and above all, sanity."
Founder of MadeWithML
"ZenML allows orchestrating ML pipelines independent of any infrastructure or tooling choices. ML teams can free their minds of tooling FOMO from the fast-moving MLOps space, with the simple and extensible ZenML interface. No more vendor lock-in, or massive switching costs!"
Former Chief Scientist Salesforce and Founder of You.com
Thanks to ZenML we've set up a pipeline where before we had only jupyter notebooks. It helped us tremendously with data and model versioning and we really look forward to any future improvements!
Machine Learning Engineer at WiseTech Global