Build portable, production-ready MLOps pipelines
A simple yet powerful pythonic framework that scales your MLOps stack with your needs.
ZenML Explained
3 mins
Create Your Pipeline
ZenML provides an easy-to-use interface to create tool and infrastructure agnostic pipelines for batch processing, continuous training, and deployment. Quickly build, execute and interact with completed runs through ZenML.
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HOW IT WORKS
Get started quickly
It's just a matter of two lines to get first practical experience with ZenML.
$pip install zenml
$zenml go
PROBLEM SOLVING
Integrate with any tools you need
ZenML lets you integrate with your favourite tools. You can alsoimplement your own integration.
Our Roadmap
Why our users love ZenML
Gabriel Martin
@gabrielmbmb_
ZenML allows you to keep your ML pipeline code cloud-agnostic, enabling faster future migrations to another technology stack. The management of the metadata and artifacts generated at each step is seamless, and allows the user to extend the framework if needed without much effort.
Machine Learning Engineer at Frontiers
Matt Squire
@FuzzyLabsAI
A lot of our teams struggle to bring sanity to their model training processes. ZenML is built in a way that encourages good, maintainable pipelines. It makes it easy to coordinate all the various tools, systems and assets that go into training ML models and, crucially, it gives you as much control over the process as you need.
CTO at Fuzzy Labs
Richard Socher
@RichardSocher
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!
Chief Scientist Salesforce and Founder of You.com
Chris Manning
@chrmanning
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
Goku Mohandas
@GokzMohandas
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
Frequently Asked Questions
Q: Why should I use ZenML?
The MLOps landscape is complex and hard to navigate. If the wrong decisions are made while creating your technology stack early on, it leads to complexity and technical debt as you progress through the stages of MLOps maturity. ZenML allows you to define your ML workflows in a vendor- and tool- agnostic manner, so that you can write your code knowing that you can switch out ML components easily as and when it is needed. Read more at https://blog.zenml.io/why-zenml/
Still not clear?
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Q: How does ZenML compare to orchestrators (Airflow, Kubeflow, Flyte, etc.)?
At a high level, an orchestrator in MLOps is a tool that enable developers to write, schedule, monitor, and manage workflows. ZenML is not an orchestrator, but rather is a tool that lets you write pipelines that can be run on multiple orchestration systems. There are standard orchestrators that ZenML supports out-of-the-box, but you are encourage to write your own orchestrator in order to gain more control as to exactly how your pipelines are executed. ZenML not only allows you to easily swap our orchestrators, but also then focuses on adding value within the pipeline itself, with features such as model deployment, metadata tracking, and management of the configuration of your MLOps stack.
Still not clear?
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Q: How can I be sure you'll stick around as a tool?
The team behind ZenML have a shared vision of making MLOps simple and accessible to accelerate problem solving in the world. We recently raised our seed round to fulfill this vision, and you can be sure we're here to stay! Plus, ZenML is and always will be an open-source effort, which lowers the risk of it just going away any time soon.
Still not clear?
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Q: Can I use tool X? How does tool Y integrate with ZenML?
Take a look at our examples directory on the GitHub, which showcases detailed examples for each integration that ZenML supports out-of-the-box. The ZenML team and community is constantly working to include more tools and integrations to the above list (check out the roadmap for more details). You can upvote features you'd like and add your ideas to the roadmap (https://zenml.io/roadmap)
Still not clear?
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Can’t find the answer to your question?
Read more on our Docs
ZenML CLOUD
Manage MLOps for teams with ZenML Cloud
Manage MLOps for Teams with ZenML Cloud
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