Alternatives to MLflow logo

Alternatives to MLflow

Kubeflow, Airflow, TensorFlow, DVC, and Seldon are the most popular alternatives and competitors to MLflow.
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What is MLflow and what are its top alternatives?

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
MLflow is a tool in the Machine Learning Tools category of a tech stack.
MLflow is an open source tool with 62 GitHub stars and 26 GitHub forks. Here鈥檚 a link to MLflow's open source repository on GitHub

Top Alternatives to MLflow

MLflow alternatives & related posts

Kubeflow logo

Kubeflow

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Machine Learning Toolkit for Kubernetes
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CONS OF KUBEFLOW
    No cons available

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    Airflow logo

    Airflow

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    A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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    Shared insights
    on
    JenkinsJenkinsAirflowAirflow

    I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

    1. Trigger Matillion ETL loads
    2. Trigger Attunity Replication tasks that have downstream ETL loads
    3. Trigger Golden gate Replication Tasks
    4. Shell scripts, wrappers, file watchers
    5. Event-driven schedules

    I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

    See more

    I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

    For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

    See more
    TensorFlow logo

    TensorFlow

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    Open Source Software Library for Machine Intelligence
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    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 970.6K views

    Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

    At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

    TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

    Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

    https://eng.uber.com/horovod/

    (Direct GitHub repo: https://github.com/uber/horovod)

    See more

    In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

    Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

    !

    See more
    DVC logo

    DVC

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    19
    0
    Open-source Version Control System for Machine Learning Projects
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    + 1
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    PROS OF DVC
      No pros available
      CONS OF DVC
        No cons available

        related DVC posts

        Seldon logo

        Seldon

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        Open-source predictive analytics and recommendation engine
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        PROS OF SELDON
          No pros available
          CONS OF SELDON
            No cons available

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            Metaflow logo

            Metaflow

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            13
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            Build and manage real-life data science projects with ease
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            PROS OF METAFLOW
              No pros available
              CONS OF METAFLOW
                No cons available

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                Keras logo

                Keras

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                Deep Learning library for Theano and TensorFlow
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                related Keras posts

                Conor Myhrvold
                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 970.6K views

                Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

                At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

                TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

                Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

                https://eng.uber.com/horovod/

                (Direct GitHub repo: https://github.com/uber/horovod)

                See more

                I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

                See more
                scikit-learn logo

                scikit-learn

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                Easy-to-use and general-purpose machine learning in Python
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                PROS OF SCIKIT-LEARN
                CONS OF SCIKIT-LEARN

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