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Metaflow VS Activeeon

Compare Metaflow VS Activeeon and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
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Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.

Activeeon logo Activeeon

ProActive Workflows & Scheduling is a java-based cross-platform workflow scheduler and resource manager that is able to run workflow tasks in multiple languages and multiple environments: Windows, Linux, Mac, Unix, etc.
  • Metaflow Landing page
    Landing page //
    2023-03-03
  • Activeeon Landing page
    Landing page //
    2022-08-19

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.

Activeeon features and specs

No features have been listed yet.

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

Activeeon videos

+ Add

Activeeon Training Session 1: General Overview of ProActive software

More videos:

  • Review - Orchestrate Microsoft PRE-BUILT + CUSTOM AI Machine Learning with ActiveEon Workflows
  • Review - Activeeon Training Session 4: Hands on AI, ML, DL, AutoML, Visualization, Python & Jupyter

Category Popularity

0-100% (relative to Metaflow and Activeeon)

Metaflow

Activeeon

Workflow Automation
71 71%
Workflow Automation
29% 29
DevOps Tools
66 66%
DevOps Tools
34% 34
Developer Tools
67 67%
Developer Tools
33% 33
Containers As A Service
0 0%
Containers As A Service
100% 100

User comments

Share your experience with using Metaflow and Activeeon. For example, how are they different and which one is better?
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Reviews

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

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

Activeeon Reviews

9 Control-M Alternatives & Competitors In 2023
Activeeon provides user interfaces to create workflows, manage job queues, schedule work, and administer the infrastructure. It gives you a single point for control over your IT and business processes. It includes error management, notification, file handling, connectors, docker support and file handling. It can automate and schedule workloads of any size.
Source: www.softwaretestingmaterial.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.

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
View more

Activeeon mentions (0)

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

What are some alternatives?

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

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

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

Mesosphere DCOS - Mesosphere DCOS organizes your entire infrastructure as if it was a single computer.

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

Kazuhm - Manage your containerized workloads through Kazuhm's easy to use distributed computing technology. Kazuhm saves cloud costs, improves security and latency.

DepHell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump ver...

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