OpenMLOps

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OpenMLOps is an open-source machine learning framework that provides a curated set of tools for conducting ML experiments and deploying models. It offers Terraform modules for easy installation into a Kubernetes cluster.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is OpenMLOps?

OpenMLOps is a production-focused, open-source machine learning framework designed to streamline the MLOps lifecycle. It provides a carefully selected suite of open-source tools, packaged as Terraform modules, for deployment into a Kubernetes cluster. Key components include Prefect for data flow automation, Jupyter Hub for collaborative experimentation, Dask for distributed computing, Feast for feature store and serving, MLFlow for model registry and experiment tracking, and Seldon for model deployment. The framework supports multi-user environments with customizable Jupyter servers, robust authentication options, and advanced API gateway capabilities via Ambassador for exposing services and managing traffic.

Best used for

Ideal for developers and data scientists who need to establish a robust MLOps environment, automate machine learning workflows, and deploy models efficiently. Especially valuable for teams looking to leverage open-source tools for experiment tracking, feature management, and scalable model serving within a Kubernetes cluster.

Common actions

automate ML workflows

deploy ML models

track ML experiments

manage ML features

set up MLOps infrastructure

automated workflowlow-code/no-codedeepfakecollaborationopen-sourceworkflowsgithub copilot"AI Agents"face swapping

Capabilities

Key features

Prefect data flow automation
Jupyter Hub for experiments
Dask distributed computing
Feast Feature Store
MLFlow model registry
Seldon model deployment
Terraform Kubernetes modules

Target Audience

developerdata scientist

Integrations

prefectjupyter-hubdaskfeastmlflowseldonkubernetesterraform+ 1 more

Pricing & Plans

Open Source

Free

FAQs

What open-source tools are included in OpenMLOps?

OpenMLOps integrates several key open-source tools: Prefect for data flow automation, Jupyter Hub for experimentation, Dask for distributed computing, Feast for feature store, MLFlow for model registry and tracking, and Seldon for model deployment. These tools are provided as Terraform modules for easy setup.

How does OpenMLOps handle model deployment and exposure?

OpenMLOps uses Seldon for model deployment and Ambassador API Gateway as the ingress controller. Ambassador, built on Envoy Proxy, allows for sophisticated traffic management and dynamic exposure of machine learning deployments through Seldon-created configurations, including authentication and authorization.

Can I use my own Jupyter server images with OpenMLOps?

Yes, OpenMLOps supports the use of custom Jupyter server images. While a default image with common data science packages is provided, users can configure their own Docker images to meet specific library and environment requirements for their experiments within the multi-user Jupyter Hub environment.

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