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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. PredictionIO vs Streamlit

PredictionIO vs Streamlit

OverviewComparisonAlternatives

Overview

PredictionIO
PredictionIO
Stacks67
Followers110
Votes8
Streamlit
Streamlit
Stacks403
Followers407
Votes12
GitHub Stars42.1K
Forks3.9K

PredictionIO vs Streamlit: What are the differences?

PredictionIO: Open Source Machine Learning Server. PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery; Streamlit: A Python app framework built specifically for Machine Learning and Data Science teams. 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.

PredictionIO and Streamlit can be categorized as "Machine Learning" tools.

Some of the features offered by PredictionIO are:

  • 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.

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

  • Free and open source
  • Build apps in a dozen lines of Python with a simple API
  • No callbacks

PredictionIO and Streamlit are both open source tools. PredictionIO with 12.1K GitHub stars and 1.98K forks on GitHub appears to be more popular than Streamlit with 2.73K GitHub stars and 184 GitHub forks.

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Detailed Comparison

PredictionIO
PredictionIO
Streamlit
Streamlit

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

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.

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.
Free and open source; Build apps in a dozen lines of Python with a simple API; No callbacks; No hidden state; Works with TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib, Seaborn, Altair, Plotly, Bokeh, Vega-Lite, and more
Statistics
GitHub Stars
-
GitHub Stars
42.1K
GitHub Forks
-
GitHub Forks
3.9K
Stacks
67
Stacks
403
Followers
110
Followers
407
Votes
8
Votes
12
Pros & Cons
Pros
  • 8
    Predict Future
Pros
  • 11
    Fast development
  • 1
    Fast development and apprenticeship
Integrations
No integrations available
Python
Python
Plotly.js
Plotly.js
PyTorch
PyTorch
Pandas
Pandas
Bokeh
Bokeh
Keras
Keras
NumPy
NumPy
Matplotlib
Matplotlib
TensorFlow
TensorFlow
Altair GraphQL
Altair GraphQL

What are some alternatives to PredictionIO, Streamlit?

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.

scikit-learn

scikit-learn

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

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.

MLflow

MLflow

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

H2O

H2O

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.

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.

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