Alternatives to Metaflow logo

Alternatives to Metaflow

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

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.
Metaflow is a tool in the Data Science Tools category of a tech stack.
Metaflow is an open source tool with 5.1K GitHub stars and 455 GitHub forks. Here’s a link to Metaflow's open source repository on GitHub

Top Alternatives to Metaflow

  • 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. ...

  • 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. ...

  • Luigi

    Luigi

    It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. ...

  • 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. ...

  • MLflow

    MLflow

    MLflow is an open source platform for managing the end-to-end machine learning lifecycle. ...

  • Pandas

    Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...

  • NumPy

    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • Anaconda

    Anaconda

    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. ...

Metaflow alternatives & related posts

Airflow logo

Airflow

1.2K
2.1K
116
A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
1.2K
2.1K
+ 1
116
PROS OF AIRFLOW
  • 45
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 11
    Cluster of workers
  • 10
    Extensibility
  • 5
    Open source
  • 4
    Python
  • 4
    Complex workflows
  • 3
    K
  • 2
    Custom operators
  • 2
    Apache project
  • 2
    Dashboard
  • 2
    Good api
CONS OF AIRFLOW
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
  • 1
    Observability is not great when the DAGs exceed 250

related Airflow posts

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
Shared insights
on
AWS Step FunctionsAWS Step FunctionsAirflowAirflow

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?

See more
Kubeflow logo

Kubeflow

149
474
16
Machine Learning Toolkit for Kubernetes
149
474
+ 1
16
PROS OF KUBEFLOW
  • 8
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 2
    Google backed
CONS OF KUBEFLOW
    Be the first to leave a con

    related Kubeflow posts

    Biswajit Pathak
    Project Manager at Sony · | 5 upvotes · 29.6K views

    Can you please advise which one to choose FastText Or Gensim, in terms of:

    1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
    2. Performance
    3. Customization of Intermediate steps
    4. FastText and Gensim both have the same underlying libraries
    5. Use cases each one tries to solve
    6. Unsupervised Vs Supervised dimensions
    7. Ease of Use.

    Please mention any other points that I may have missed here.

    See more

    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)?

    See more
    Luigi logo

    Luigi

    61
    152
    8
    ETL and data flow management library
    61
    152
    + 1
    8
    PROS OF LUIGI
    • 5
      Hadoop Support
    • 2
      Python
    • 1
      Open soure
    CONS OF LUIGI
      Be the first to leave a con

      related Luigi posts

      TensorFlow logo

      TensorFlow

      2.7K
      2.9K
      77
      Open Source Software Library for Machine Intelligence
      2.7K
      2.9K
      + 1
      77
      PROS OF TENSORFLOW
      • 25
        High Performance
      • 16
        Connect Research and Production
      • 13
        Deep Flexibility
      • 9
        True Portability
      • 9
        Auto-Differentiation
      • 2
        Easy to use
      • 2
        High level abstraction
      • 1
        Powerful
      CONS OF TENSORFLOW
      • 9
        Hard
      • 6
        Hard to debug
      • 1
        Documentation not very helpful

      related TensorFlow posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M 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—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s 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’s deep learning toolkit which makes it easier to start—and speed up—distributed 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
      MLflow logo

      MLflow

      133
      393
      8
      An open source machine learning platform
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      393
      + 1
      8
      PROS OF MLFLOW
      • 4
        Simplified Logging
      • 4
        Code First
      CONS OF MLFLOW
        Be the first to leave a con

        related MLflow posts

        Shared insights
        on
        MLflowMLflowDVCDVC

        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 the 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?

        See more
        Biswajit Pathak
        Project Manager at Sony · | 5 upvotes · 29.6K views

        Can you please advise which one to choose FastText Or Gensim, in terms of:

        1. Operability with ML Ops tools such as MLflow, Kubeflow, etc.
        2. Performance
        3. Customization of Intermediate steps
        4. FastText and Gensim both have the same underlying libraries
        5. Use cases each one tries to solve
        6. Unsupervised Vs Supervised dimensions
        7. Ease of Use.

        Please mention any other points that I may have missed here.

        See more
        Pandas logo

        Pandas

        1.2K
        1K
        20
        High-performance, easy-to-use data structures and data analysis tools for the Python programming language
        1.2K
        1K
        + 1
        20
        PROS OF PANDAS
        • 19
          Easy data frame management
        • 1
          Extensive file format compatibility
        CONS OF PANDAS
          Be the first to leave a con

          related Pandas 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
          Guillaume Simler

          Jupyter Anaconda Pandas IPython

          A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

          The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

          See more
          NumPy logo

          NumPy

          932
          623
          7
          Fundamental package for scientific computing with Python
          932
          623
          + 1
          7
          PROS OF NUMPY
          • 6
            Great for data analysis
          • 1
            Faster than list
          CONS OF NUMPY
            Be the first to leave a con

            related NumPy 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
            Anaconda logo

            Anaconda

            349
            384
            0
            The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
            349
            384
            + 1
            0
            PROS OF ANACONDA
              Be the first to leave a pro
              CONS OF ANACONDA
                Be the first to leave a con

                related Anaconda posts

                Shared insights
                on
                JavaJavaAnacondaAnacondaPythonPython

                I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud

                See more
                Guillaume Simler

                Jupyter Anaconda Pandas IPython

                A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

                The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

                See more