PredictionIO vs TensorFlow Comparison | StackShare

Overview

PredictionIO

Stacks67

Followers110

Votes8

TensorFlow

Stacks3.8K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

PredictionIO vs TensorFlow: What are the differences?

Developers describe PredictionIO as "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. On the other hand, TensorFlow is detailed as "Open Source Software Library for Machine Intelligence". 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.

PredictionIO and TensorFlow can be primarily classified as "Machine Learning" tools.

"Predict Future" is the primary reason why developers consider PredictionIO over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.

PredictionIO is an open source tool with 11.8K GitHub stars and 1.92K GitHub forks. Here's a link to PredictionIO's open source repository on GitHub.

Uber Technologies, 9GAG, and StyleShare Inc. are some of the popular companies that use TensorFlow, whereas PredictionIO is used by 500 Startups, Betaout, and Tokopedia. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to PredictionIO, which is listed in 5 company stacks and 5 developer stacks.

Advice on PredictionIO, TensorFlow

Xi

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.63k views4.63k

Comments

Detailed Comparison

PredictionIO	TensorFlow
PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.	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.
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 192.3K
GitHub Forks -	GitHub Forks 74.9K
Stacks 67	Stacks 3.8K
Followers 110	Followers 3.5K
Votes 8	Votes 106
Pros & Cons
Pros 8 Predict Future	Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful
Integrations
No integrations available	JavaScript

What are some alternatives to PredictionIO, TensorFlow?

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 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

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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

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

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

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

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

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.