MLOps Workflow & Pipeline Orchestration Tools

An open-source, container-native workflow engine for orchestrating parallel jobs on Kubernetes, part of the broader Argo Project alongside CD, Rollouts and Events.

A unified Python interface for constructing and managing workflows across engines such as Argo Workflows, Tekton and Apache Airflow, adopted internally at Ant Group. The GitHub repository's last commit was in October 2024, so check its activity before adopting it.

Simplifies deploying Jupyter Notebooks as Kubeflow Pipelines workflows directly from a notebook, with a v2 release shipping in 2026 for current Kubeflow Pipelines.

An AI orchestration platform written in pure Python for dynamic, resilient workflows at scale, with 80M+ downloads and adoption at Fortune 500 companies.

An asset-centric data orchestrator for ML, analytics and ETL that tracks lineage and data health across a pipeline rather than just task completion.

Turns any Python function into an observable workflow with a single decorator, backed by an open-source core plus managed Prefect Cloud for production.

Spotify's Python package for building complex pipelines of batch jobs, handling dependency resolution, workflow management and failure recovery.

An open-source AI orchestration framework managing ML and generative AI applications across their lifecycle, also available as a managed service on the Iguazio platform.

An extensible open-source MLOps framework for reproducible pipelines, now positioned as a unified platform spanning classical ML, LLM pipelines and AI agents.

A Python framework hosted by the LF AI & Data Foundation for creating reproducible, maintainable and modular data engineering and data science code.

Originally built at Netflix, Metaflow makes it quick to build and manage real-life ML, AI and data science projects from a laptop to the cloud without code changes.

An MLOps platform for AI products that combine LLMs and specialized models, automating everything from dataset versioning to pipeline execution and deployment.

Runs with all popular deep learning frameworks and machine learning libraries on Kubernetes, covering tracking, orchestration, optimization and model management.

Manages ML and generative AI workflows end-to-end on the Iguazio Data Science Platform, built on top of the open-source MLRun framework.

An inference platform for deploying any model anywhere, combining developer-friendly packaging with tailored optimization, autoscaling and streamlined operations.