There is no one-size-fits-all MLOps tooling stack. ZenML decouples the pipeline from the stack with simple abstractions. Configure and share your stacks using the CLI and seamlessly switch between local development and full-on production stacks. The entire data science and machine learning team can share these stacks. These stacks will supercharge your steps and pipelines with extra functionality .
Say goodbye to vendor and tool lock-in. ZenML runs out-of-the-box with many powerful orchestrators like Kubeflow and Airflow. If the tools that you want to use do not have a ZenML integration, we’ve made it easy for you to do it yourself. ZenML is open-source and fully extensible on top of powerful component abstractions.
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.
There is no one-size-fits-all MLOps tooling stack. ZenML decouples the pipeline from the stack with simple abstractions. Configure and share your stacks using the CLI and seamlessly switch between local development and full-on production stacks. The entire data science and machine learning team can share these stacks. These stacks will supercharge your steps and pipelines with extra functionality .
Say goodbye to vendor and tool lock-in. ZenML runs out-of-the-box with many powerful orchestrators like Kubeflow and Airflow. If the tools that you want to use do not have a ZenML integration, we’ve made it easy for you to do it yourself. ZenML is open-source and fully extensible on top of powerful component abstractions.
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.
There is no one-size-fits-all MLOps tooling stack. ZenML decouples the pipeline from the stack with simple abstractions. Configure and share your stacks using the CLI and seamlessly switch between local development and full-on production stacks. The entire data science and machine learning team can share these stacks. These stacks will supercharge your steps and pipelines with extra functionality .
Say goodbye to vendor and tool lock-in. ZenML runs out-of-the-box with many powerful orchestrators like Kubeflow and Airflow. If the tools that you want to use do not have a ZenML integration, we’ve made it easy for you to do it yourself. ZenML is open-source and fully extensible on top of powerful component abstractions.
Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly.
Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change.
Keep up with the latest changes in the MLOps world and easily integrate any new developments.
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
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
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
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
@GokuMohandas
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
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
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
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
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
@GokuMohandas
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
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
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
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
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
@GokuMohandas
ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibitiliy, and above all, sanity.