The bridge between ML and Ops

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.

ZenML's automatic logging and containerization have transformed our MLOps pipeline. We've drastically reduced environment inconsistencies and can now reproduce any experiment with just a few clicks. It's like having a DevOps engineer built into our ML framework.

"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"

"ZenML has transformed how we manage our GPU resources. The automatic deployment and shutdown of GPU instances have significantly reduced our cloud costs. We're no longer paying for idle GPUs, and our team can focus on model development instead of infrastructure management. It's made our ML operations much more efficient and cost-effective."

"After a benchmark on several solutions, we choose ZenML for its stack flexibility and its incremental process. We started from small local pipelines and gradually created more complex production ones. It was very easy to adopt."

"ZenML allowed us a fast transition between dev to prod. It’s no longer the big fish eating the small fish – it’s the fast fish eating the slow fish."

"ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibitiliy, and above all, sanity."

"ZenML's approach to standardization and reusability has been a game-changer for our ML teams. We've significantly reduced development time with shared components, and our cross-team collaboration has never been smoother. The centralized asset management gives us the visibility and control we needed to scale our ML operations confidently."

"With ZenML, we're no longer tied to a single cloud provider. The flexibility to switch backends between AWS and GCP has been a game-changer for our team."

"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!"

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!