Metaflow Alternatives
Metaflow is described as 'human-friendly Python library that helps scientists and engineers build and manage real-life data science projects' and is a workflow automation tool. There are more than 10 alternatives to Metaflow for a variety of platforms, including Linux, SaaS, Heroku, Mac and Windows. The best alternative is RunDeck, which is both free and Open Source. Other great apps like Metaflow are Apache Airflow, StackStorm, Shipyard App and Zenaton.
Metaflow alternatives are mainly Workflow Automation Tools but may also be Server Management Tools or Task Automation Apps. Filter by these if you want a narrower list of alternatives or looking for a specific functionality of Metaflow.RunDeck is an open source automation service with a web console, command line tools and a WebAPI. It lets you easily run automation tasks across a set of nodes.
Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
- Free • Open Source
- Linux
- Self-Hosted
StackStorm is a powerful open-source automation platform that wires together all of your apps, services and workflows. It’s extendable, flexible, and built with love for DevOps and ChatOps.
No screenshots yet- Freemium • Proprietary
- Online
- Software as a Service (SaaS)
Shipyard is a cloud-based workflow automation platform that removes complexity and increases visibility of automation efforts. It empowers Data Teams to focus on launching, monitoring, and sharing their business solutions without the need for DevOps.
- Freemium • Proprietary
- Clever Cloud
- Software as a Service (SaaS)
- Heroku
Zenaton is a developer tool and hosted workflow engine for writing, running and monitoring all of your background processes whether it is a single task or a long running workflow.
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
The Valohai platform makes machine learning in production easy. Data scientists and machine learning engineers can work together to build end-to-end machine learning pipelines that take in data, train a model, and deploy to production automatically.