Need advice about which tool to choose?Ask the StackShare community!

Polyaxon

11
65
+ 1
14
scikit-learn

1.2K
1.1K
+ 1
44
Add tool

Polyaxon vs scikit-learn: What are the differences?

Polyaxon: An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications. An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications; scikit-learn: Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

Polyaxon and scikit-learn belong to "Machine Learning Tools" category of the tech stack.

scikit-learn is an open source tool with 36K GitHub stars and 17.6K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

Decisions about Polyaxon and scikit-learn

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Polyaxon
Pros of scikit-learn
  • 2
    Cli
  • 2
    API
  • 2
    Streamlit integration
  • 2
    Python Client
  • 2
    Notebook integration
  • 2
    Tensorboard integration
  • 2
    VSCode integration
  • 25
    Scientific computing
  • 19
    Easy

Sign up to add or upvote prosMake informed product decisions

Cons of Polyaxon
Cons of scikit-learn
    Be the first to leave a con
    • 2
      Limited

    Sign up to add or upvote consMake informed product decisions

    What is Polyaxon?

    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

    What is scikit-learn?

    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Polyaxon?
    What companies use scikit-learn?
    See which teams inside your own company are using Polyaxon or scikit-learn.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Polyaxon?
    What tools integrate with scikit-learn?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    GitHubPythonReact+42
    49
    40725
    What are some alternatives to Polyaxon and scikit-learn?
    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.
    MLflow
    MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
    Pachyderm
    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
    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.
    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.
    See all alternatives