Flyte

Create and run data science and machine learning workflows using Python

Cost

Free

Rating

★ People love it

Time to value

Moderate Setup (1-3 hours)

You can use Flyte to create and run data science and machine learning workflows using Python. It handles task orchestration, automatic retries, and failure recovery across Kubernetes clusters. You can write workflows locally, test them, then deploy to production. It tracks data lineage, manages resources dynamically, and lets you build reusable components. Teams use it for training models, processing large datasets, and creating data pipelines that can recover from crashes and continue running.

What Flyte does

Define tasks and workflows using Python decoratorsSet resource requirements for CPU and memoryCreate conditional logic and loops in workflowsTrack data lineage across workflow executionsDebug workflows locally before production deploymentMonitor workflow execution with notificationsScale compute resources based on workload demandsReuse components across different projectsWrite workflows in pure Python syntaxAutomatic crash recovery and checkpointingDynamic resource allocation at runtimeTest locally then deploy to KubernetesBuilt-in data lineage trackingReusable workflow components across teamsNative support for loops and conditionalsLive remote debugging capabilities

Pricing breakdown

PlanPrice10 seats / yr

Open Source$0—

Annual estimates assume continuous billing at the listed list price. Volume discounts typical above 50 seats.

Tutorials & Demos

Frequently asked

Pandas, Scikit-learn, SQLAlchemy, Plotly

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