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Metaflow

6
21
+ 1
0
NumPy

742
535
+ 1
5
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NumPy vs Metaflow: What are the differences?

Developers describe NumPy as "Fundamental package for scientific computing with Python". 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. On the other hand, Metaflow is detailed as "Build and manage real-life data science projects with ease". 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.

NumPy and Metaflow can be primarily classified as "Data Science" tools.

Some of the features offered by NumPy are:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code

On the other hand, Metaflow provides the following key features:

  • End-to-end ML Platform
  • Model with your favorite tools
  • Powered by the AWS cloud

NumPy and Metaflow are both open source tools. NumPy with 13.5K GitHub stars and 4.44K forks on GitHub appears to be more popular than Metaflow with 3.18K GitHub stars and 230 GitHub forks.

Pros of Metaflow
Pros of NumPy
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      Great for data analysis

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    What is 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.

    What is 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.

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    What companies use Metaflow?
    What companies use NumPy?

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    What tools integrate with Metaflow?
    What tools integrate with NumPy?
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      What are some alternatives to Metaflow and NumPy?
      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
      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
      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 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 is an open source platform for managing the end-to-end machine learning lifecycle.
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