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StackStorm VS Metaflow

Compare StackStorm VS Metaflow and see what are their differences

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StackStorm logo StackStorm

StackStorm is a powerful open-source automation platform that wires together all of your apps...

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.
  • StackStorm Landing page
    Landing page //
    2023-07-27
  • Metaflow Landing page
    Landing page //
    2023-03-03

StackStorm features and specs

  • Open Source
    StackStorm is an open-source automation platform, which means it is cost-effective as there are no licensing fees. Users can contribute to its development and customization.
  • Event-Driven Automation
    It supports event-driven automation, allowing users to automate complex workflows that can be triggered by events from numerous sources.
  • Flexible and Extensible
    StackStorm provides a high level of flexibility and extensibility through integrations and customizable actions, allowing it to fit various automation needs.
  • Active Community
    There is an active community supporting StackStorm, which can be beneficial for troubleshooting, sharing best practices, and seeking advice from other users.
  • Self-Service Automation
    With its GUI and command-line tools, StackStorm enables self-service automation, empowering users to initiate and manage automation tasks without deep technical expertise.

Possible disadvantages of StackStorm

  • Complex Setup
    The initial setup and configuration of StackStorm can be complex and time-consuming, requiring a good understanding of the system and its dependencies.
  • Steeper Learning Curve
    Users may experience a steep learning curve due to the wide range of features and the requirement for understanding underlying technologies like Python and YAML.
  • Dependency Management
    Managing dependencies and ensuring compatibility between different integrations and components can be challenging and require continual maintenance.
  • Resource Intensive
    Running StackStorm can be resource-intensive, which might require additional infrastructure or cloud resources, especially for large-scale deployments.
  • Limited Support
    While there is a community for support, professional support options may be limited compared to proprietary solutions, which might be a concern for enterprises needing guaranteed, reliable support.

Metaflow features and specs

  • Ease of Use
    Metaflow is designed with a strong focus on user experience, providing users with a simple and user-friendly interface for building and managing workflows. Its Pythonic API makes it easy for data scientists to work with complex data workflows without needing to learn a lot of new concepts.
  • Scalability
    Metaflow supports scalable data workflows, allowing users to run their workflows seamlessly from a laptop to the cloud. It integrates well with AWS, enabling users to utilize Amazon's scalable infrastructure for processing large datasets.
  • Versioning
    Metaflow provides built-in support for data and model versioning, making it easier for teams to track changes and reproduce results. This feature is crucial for maintaining consistency and reliability in machine learning projects.
  • Integration with Popular Tools
    Metaflow integrates well with popular data science and machine learning tools, including Jupyter notebooks and AWS services, enhancing its usability within existing data ecosystems.
  • Error Handling and Monitoring
    Metaflow offers robust error handling and monitoring capabilities, allowing users to track the execution of workflows, identify errors, and debug issues efficiently.

Possible disadvantages of Metaflow

  • AWS Dependency
    While Metaflow supports other infrastructures, it is tightly integrated with AWS. Users who do not use AWS may find it less convenient compared to other tools that are more agnostic in their cloud support.
  • Limited Support for Non-Python Environments
    Metaflow primarily supports Python, which might be a limitation for teams or projects that rely heavily on other programming languages for their workflows.
  • Learning Curve for Advanced Features
    Although Metaflow is designed to be user-friendly, utilizing its advanced features and realizing its full potential can have a steep learning curve, especially for users without prior experience with workflow management systems.
  • Community and Ecosystem Size
    Compared to some of its competitors, Metaflow has a smaller community and ecosystem, which might limit the availability of third-party resources, plugins, and community support.
  • Enterprise Features
    Some advanced enterprise features, while robust, may not be as developed or extensive compared to other dedicated data processing and workflow management platforms.

StackStorm videos

+ Add

StackStorm 101 (v0.12)

More videos:

  • Review - Recovery from OpenStack Failures using Nagios and StackStorm

Metaflow videos

+ Add

useR! 2020: End-to-end machine learning with Metaflow (S. Goyal, B. Galvin, J. Ge), tutorial

More videos:

  • Review - Screencast: Metaflow Sandbox Example

Category Popularity

0-100% (relative to StackStorm and Metaflow)

StackStorm

Metaflow

Automation
66 66%
Automation
34% 34
Workflow Automation
39 39%
Workflow Automation
61% 61
DevOps Tools
27 27%
DevOps Tools
73% 73
Web Service Automation
100 100%
Web Service Automation
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare StackStorm and Metaflow

StackStorm Reviews

We have no reviews of StackStorm yet.
Be the first one to post

Metaflow Reviews

Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Metaflow enables you to define your pipeline as a child class of FlowSpec that includes class methods with step decorators in Python code.
Source: medium.com

Social recommendations and mentions

Based on our record, Metaflow seems to be more popular. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

StackStorm mentions (0)

We have not tracked any mentions of StackStorm yet. Tracking of StackStorm recommendations started around Mar 2021.

Metaflow mentions (14)

  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / 6 months ago
  • Recapping the AI, Machine Learning and Computer Meetup — August 15, 2024
    As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 9 months ago
  • What are some open-source ML pipeline managers that are easy to use?
    I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 2 years ago
  • Needs advice for choosing tools for my team. We use AWS.
    1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 2 years ago
  • Selfhosted chatGPT with local contente
    Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: about 2 years ago
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What are some alternatives?

When comparing StackStorm and Metaflow, you can also consider the following products

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

RunDeck - RunDeck is an open source automation service with a web console, command line tools and a WebAPI.

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

Pipedream - Integration platform for developers

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