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  5. H2O vs PredictionIO vs scikit-learn

H2O vs PredictionIO vs scikit-learn

OverviewDecisionsComparisonAlternatives

Overview

PredictionIO
PredictionIO
Stacks67
Followers110
Votes8
scikit-learn
scikit-learn
Stacks1.3K
Followers1.1K
Votes45
GitHub Stars63.9K
Forks26.4K
H2O
H2O
Stacks122
Followers211
Votes8
GitHub Stars7.3K
Forks2.0K

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Advice on PredictionIO, scikit-learn, H2O

cfvedova
cfvedova

Oct 10, 2020

Decided

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.

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Detailed Comparison

PredictionIO
PredictionIO
scikit-learn
scikit-learn
H2O
H2O

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

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

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.;Customize the modularized open codebase to fulfill any unique prediction requirement.;Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.;Build powerful features in minutes, not months. Streamline the data engineering process.
--
Statistics
GitHub Stars
-
GitHub Stars
63.9K
GitHub Stars
7.3K
GitHub Forks
-
GitHub Forks
26.4K
GitHub Forks
2.0K
Stacks
67
Stacks
1.3K
Stacks
122
Followers
110
Followers
1.1K
Followers
211
Votes
8
Votes
45
Votes
8
Pros & Cons
Pros
  • 8
    Predict Future
Pros
  • 26
    Scientific computing
  • 19
    Easy
Cons
  • 2
    Limited
Pros
  • 2
    Highly customizable
  • 2
    Very fast and powerful
  • 2
    Super easy to use
  • 2
    Auto ML is amazing
Cons
  • 1
    Not very popular

What are some alternatives to PredictionIO, scikit-learn, H2O?

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.

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/

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.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

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

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

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

Gluon

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Comet.ml

Comet.ml

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

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