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ML Pipeline Orchestration

Manage your workflow end-to-end using a user-friendly environment, featuring fully integrated workflow management, experiment tracking and AutoML tools

Automated and Scalable
Pipeline Orchestration

Consume MLRun, Iguazio’s open source framework, to orchestrate ML pipelines from the research stage to production-ready AI applications. With a feature store and a modular strategy, MLRun enables a simple, continuous, and automated way of creating scalable production pipelines. MLRun automates the build process, execution, data movement, scaling, versioning, parameterization, outputs tracking, CI/CD integration, deployment to production, monitoring, and more.

AutoML

AutoML

Run multiple experiments in parallel, each using a different combination of algorithm functions and parameter sets (hyper-parameters) to automatically select the best result.

Feature Engineering Made Simple

Experiment Tracking

Describe and track code, metadata, inputs and outputs of machine learning related tasks (executions) and re-use results with a generic and easy-to-use mechanism.

Online and Offline Feature Store

Online and Offline Feature Store

Maintain the same set of features in the training and inferencing (real-time) stages with MLRun’s unified feature store.

Workflows (Kubeflow)

Workflows (Kubeflow)

Natively integrate with Kubeflow Pipelines to compose, deploy and manage end-to-end machine learning workflows with UI and a set of services.

Benefits

Cut Time to Production

Cut Time to Production

Automate MLOps

Automate MLOps

Track and Reproduce

Track and Reproduce

Collaborate and Re-Use

Collaborate and Re-Use

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Data Science Platform Tutorials

Platform Overview

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Data Science Platform Documentation

Documentation

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Accelerate your ML pipelines

Learn how you can automate, scale and orchestrate pipelines end to end with the Iguazio MLOps Platform