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 74 GitHub stars and 34 GitHub forks. Here鈥檚 a link to MLflow's open source repository on GitHub

Top Alternatives to MLflow

  • Kubeflow

    Kubeflow

    The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. ...

  • Airflow

    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • TensorFlow

    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • DVC

    DVC

    It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. ...

  • Seldon

    Seldon

    Seldon is an Open Predictive Platform that currently allows recommendations to be generated based on structured historical data. It has a variety of algorithms to produce these recommendations and can report a variety of statistics. ...

  • Metaflow

    Metaflow

    It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. ...

  • PyTorch

    PyTorch

    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...

  • Keras

    Keras

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...

MLflow alternatives & related posts

Kubeflow logo

Kubeflow

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435
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Machine Learning Toolkit for Kubernetes
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435
+ 1
13
PROS OF KUBEFLOW
  • 5
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed
CONS OF KUBEFLOW
    Be the first to leave a con

    related Kubeflow posts

    Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

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    Airflow logo

    Airflow

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    1.8K
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    A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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    107
    PROS OF AIRFLOW
    • 42
      Features
    • 13
      Task Dependency Management
    • 12
      Beautiful UI
    • 10
      Cluster of workers
    • 9
      Extensibility
    • 5
      Open source
    • 4
      Complex workflows
    • 4
      Python
    • 2
      Custom operators
    • 2
      K
    • 2
      Dashboard
    • 1
      Good api
    • 1
      Apache project
    CONS OF AIRFLOW
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      related Airflow posts

      Shared insights
      on
      Jenkins
      Airflow

      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
      Shared insights
      on
      AWS Step Functions
      Airflow

      I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.

      I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.

      I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?

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      TensorFlow logo

      TensorFlow

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      2.7K
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      Open Source Software Library for Machine Intelligence
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      2.7K
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      PROS OF TENSORFLOW
      • 24
        High Performance
      • 16
        Connect Research and Production
      • 13
        Deep Flexibility
      • 9
        Auto-Differentiation
      • 9
        True Portability
      • 2
        Easy to use
      • 2
        High level abstraction
      • 1
        Powerful
      CONS OF TENSORFLOW
      • 8
        Hard
      • 5
        Hard to debug
      • 1
        Documentation not very helpful

      related TensorFlow posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M 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

      23
      52
      1
      Open-source Version Control System for Machine Learning Projects
      23
      52
      + 1
      1
      PROS OF DVC
      • 1
        Full reproducibility
      CONS OF DVC
        Be the first to leave a con

        related DVC posts

        Shared insights
        on
        MLflow
        DVC

        I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use MLflow artifact store for this purpose. Or maybe these two serve different purposes and it may be good to do both! Can anyone help please?

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        Seldon logo

        Seldon

        10
        29
        0
        Open-source predictive analytics and recommendation engine
        10
        29
        + 1
        0
        PROS OF SELDON
          Be the first to leave a pro
          CONS OF SELDON
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            related Seldon posts

            Metaflow logo

            Metaflow

            6
            24
            0
            Build and manage real-life data science projects with ease (by Netflix)
            6
            24
            + 1
            0
            PROS OF METAFLOW
              Be the first to leave a pro
              CONS OF METAFLOW
                Be the first to leave a con

                related Metaflow posts

                PyTorch logo

                PyTorch

                872
                967
                33
                A deep learning framework that puts Python first
                872
                967
                + 1
                33
                PROS OF PYTORCH
                • 11
                  Easy to use
                • 9
                  Developer Friendly
                • 8
                  Easy to debug
                • 5
                  Sometimes faster than TensorFlow
                CONS OF PYTORCH
                • 2
                  Lots of code

                related PyTorch posts

                Server side

                We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

                • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

                • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

                • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

                Client side

                • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

                • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

                • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

                Cache

                • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

                Database

                • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

                Infrastructure

                • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

                Other Tools

                • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

                • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

                See more
                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M 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
                Keras logo

                Keras

                872
                907
                12
                Deep Learning library for Theano and TensorFlow
                872
                907
                + 1
                12
                PROS OF KERAS
                • 5
                  Quality Documentation
                • 4
                  Easy and fast NN prototyping
                • 3
                  Supports Tensorflow and Theano backends
                CONS OF KERAS
                • 3
                  Hard to debug

                related Keras posts

                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M 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.

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