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Linux Foundation: The MLflow Project and Zephyr Project

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  • Databricks Donates MLflow Project To Linux Foundation

    At the Spark + AI Summit virtual event, Databricks has announced that the MLflow project is joining the Linux Foundation.

  • The MLflow Project Joins Linux Foundation
  • The MLflow Project Joins Linux Foundation
  • The MLflow Project Joins Linux Foundation

    The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced that MLflow, an open source machine learning (ML) platform created by Databricks, will join the Linux Foundation. Since its introduction at Spark + AI Summit two years ago, MLflow has experienced impressive community engagement from over 200 contributors and is downloaded more than 2 million times per month, with a 4x annual growth rate in downloads. The Linux Foundation provides a vendor neutral home with an open governance model to broaden adoption and contributions to the MLflow project even further.

    “The steady increase in community engagement shows the commitment data teams have to building the machine learning platform of the future. The rate of adoption demonstrates the need for an open source approach to standardizing the machine learning lifecycle,” said Michael Dolan, VP of Strategic Programs at the Linux Foundation. “Our experience in working with the largest open source projects in the world shows that an open governance model allows for faster innovation and adoption through broad industry contribution and consensus building.”

  • The Zephyr Project Marks Critical Milestones for Security and Product-Ready Maturity
  • The Zephyr Project Marks Critical Milestones for Security and Product-Ready Maturity

    The Zephyr™ Project, an open source project at the Linux Foundation that builds a safe, secure and flexible real-time operating system (RTOS) for the Internet of Things (IoT) in space-constrained devices, announces continued momentum by marking critical milestones for security and product-ready maturity.

    Earlier this year, the NCC Group, a global expert in cyber security and risk mitigation, notified the Zephyr Project of a number of security issues found as part of their independent research into the security posture of Zephyr. The research, which was driven by growing interest from their clients, found Zephyr to be a mature, and a highly active and growing project with increasing market share. The May 2020 report outlines the issues discovered in detail and acknowledges the proactive work of the Zephyr Project Security Committee to fix these issues and follow-up on recommendations of the report. Priority fixes have been backported into Zephyr's Long Term Support (LTS) and a maintenance release published. Learn more about Zephyr's security assessment and response in this blog.

IDG's coverage

  • MLflow is now a Linux Foundation project

    Databricks, the company behind the commercial development of Apache Spark, is placing its machine learning lifecycle project MLflow under the stewardship of the Linux Foundation.

    MLflow provides a programmatic way to deal with all the pieces of a machine learning project through all its phases — construction, training, fine-tuning, deployment, management, and revision. It tracks and manages the the datasets, model instances, model parameters, and algorithms used in machine learning projects, so they can be versioned, stored in a central repository, and repackaged easily for reuse by other data scientists.

MLflow is now a Linux Foundation project

  • MLflow is now a Linux Foundation project

    Databricks, the company behind the commercial development of Apache Spark, is placing its machine learning lifecycle project MLflow under the stewardship of the Linux Foundation.

    MLflow provides a programmatic way to deal with all the pieces of a machine learning project through all its phases — construction, training, fine-tuning, deployment, management, and revision.

    It tracks and manages the the datasets, model instances, model parameters, and algorithms used in machine learning projects, so they can be versioned, stored in a central repository, and repackaged easily for reuse by other data scientists.

MLflow Project joins Linux Foundation

  • MLflow Project joins Linux Foundation

    What’s better than a machine learning platform? Answer: an en-to-end machine learning platform, obviously.

    What’s better than an end-to-end machine learning platform? Answer: an open source end-to-end machine learning platform, obviously, obviously.

    What’s better than an open source end-to-end machine learning platform? Answer: an open source end-to-end machine learning platform that resides under the auspices of the Linux Foundation, obviously, obviously, obviously.

    Okay enough of this, but this is what Databricks is hoping — the company has now said that its MLflow open source machine learning will join the Linux Foundation.

    The project is two-years old in 2020 and has seen engagement from somewhere over 200 contributors.

    It is downloaded more than 2 million times per month.

Databricks hands over MLFlow to Linux

  • Databricks hands over MLFlow to Linux

    Open Source MLOps framework MLFlow launched by Databricks has been moved to the Linux Foundation. The news was announced at the Spark+AI Summit by Matei Zaharia, creator of Apache Spark and MLFLow, and the co-founder of Databricks. New functionalities are added to the framework, including integration with Continuous Integration/Continuous Deployment (CI/CD) platforms like GitLab and Jenkins, new Application Programming Interfaces (API), automatic versioning and logging, etc.

MLflow has Grown Up and Left Home...

  • MLflow has Grown Up and Left Home: Machine Learning Framework Joins Linux Foundation

    “We’ve moved MLflow into the Linux Foundation as a vendor-neutral non-profit organization to manage the project long-term”

    Databricks has donated its hugely popular machine learning framework MLflow to the non-profit Linux Foundation.

    The open source tool from the US company (whose founders created analytics engine Apache Spark) sees an eye-ball popping 2.5 million downloads a month and has 200 contributors from 100 organisations.

    MLflow allows organisations to package their code for reproducible runs and execute hundreds of parallel experiments, across platforms. It integrates closely with Apache Spark, SciKit-Learn, TensorFlow and other open source ML frameworks.

Databricks moves MLflow to Linux Foundation

  • Databricks moves MLflow to Linux Foundation, introduces Delta Engine

    MLflow, the open source machine learning operations (MLOps) platform created by Databricks, is becoming a Linux Foundation project. It's also getting some new features. The move was announced by Matei Zaharia, co-founder of Databricks, and creator of both MLflow and Apache Spark, at the company's Spark + AI Summit virtual event today.

    [...]

    With that kind of growth, Zaharia explained that it's important for customers to see the project managed by a vendor-neutral organization. This protects customer investments in MLflow and eliminates any unease that the project might be dependent on Databricks' corporate direction. I still find it odd that Spark itself -- on which the Databricks platform is based -- is an Apache Software Foundation project, whereas associated projects Delta Lake and now MLflow sit under the Linux Foundation. Zaharia explained that the two foundations operate in a similar enough way that it shouldn't impact users and pointed out that Kubernetes and the Cloud Native Computing Foundation are also under the Linux Foundation umbrella, creating useful synergies for the Databricks-launched projects that have moved there.

Databricks hands MLflow to Linux Foundation

  • Databricks hands MLflow to Linux Foundation, speeds up Delta Lake, and pushes pandas on Spark forward

    Data science conference Spark+AI Summit is still in full swing. Organised by Spark experts Databricks, it naturally lent itself as a stage for some progress reports and letting the firm show off new products.

    Amongst other things, machine learning platform MLflow has found a new home at the Linux Foundation. After being in the open for two years, the move provides the Databricks project with a new vendor neutral environment in the hopes that this will lead to higher adoption rates and more outside committers.

    While this seems like a somewhat sensible thing to do, onlookers might wonder about the choice of foundation. After all, company co-founder and MLflow creator Matei Zaharia chose the Apache Software Foundation for Spark. A second glance however reveals that the Linux Foundation seems to become a bit of a default for Databricks in recent years, since the company’s Delta Lake was also handed over to the org last autumn.

IBM donates AI ethics tools to Linux Foundation

  • IBM donates AI ethics tools to Linux Foundation

    The Linux Foundation is a nonprofit technology consortium dedicated to protecting and advancing Linux, an open source operating system. The group provides support to numerous open source projects and communities. As part of the Linux Foundation, the LF AI Foundation supports open source projects in AI, machine learning and deep learning.

    Open source projects, while open to the community, are still controlled by an individual or vendor. They individual or vendor can limit who works on the project, the direction the project takes and how quickly it updates.

    With this latest move, IBM concedes control of the three AI toolkits to the vendor-neutral LF AI Foundation. Linux Foundation's open governance model, in theory, eliminates single-vendor control of open source projects, broadens the community working on them and helps accelerates their growth.

MLflow is now a Linux Foundation project

  • MLflow is now a Linux Foundation project

    Databricks, the company behind the commercial development of Apache Spark, is placing its machine learning lifecycle project MLflow under the stewardship of the Linux Foundation.

    MLflow provides a programmatic way to deal with all the pieces of a machine learning project through all its phases — construction, training, fine-tuning, deployment, management, and revision.

    It tracks and manages the the datasets, model instances, model parameters, and algorithms used in machine learning projects, so they can be versioned, stored in a central repository, and repackaged easily for reuse by other data scientists.

    [...]

    MLflow differs from Kubeflow in several key ways. For one, it doesn’t require Kubernetes as a component; it runs on local machines by way of simple Python scripts, or in Databricks’s hosted environment. And while Kubeflow focuses on TensorFlow and PyTorch as its learning systems, MLflow is agnostic — it can work with models from those frameworks and many others.

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