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Best Workflow and Pipeline Orchestration Tools: Machine Learning Guide
Kale – Aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows.
Flyte – Easy to create concurrent, scalable, and maintainable workflows for machine learning.
MLRun – Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines.
Prefect – A workflow management system, designed for modern infrastructure.
ZenML – An extensible open-source MLOps framework to create reproducible pipelines.
Argo – Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.
Kedro – Library that implements software engineering best-practice for data and ML pipelines.
Luigi – Python module that helps you build complex pipelines of batch jobs.
Metaflow – Human-friendly lib that helps scientists and engineers build and manage data science projects.
Couler – Unified interface for constructing and managing workflows on different workflow engines.
Valohai – Simple and powerful tool to train, evaluate and deploy models.
Dagster.io – Data orchestrator for machine learning, analytics, and ETL.
Netflix Genie – Genie developed by Netflix is an open-source distributed workflow/task orchestration framework.