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Description

Apache PredictionIO® is a robust open-source machine learning server designed for developers and data scientists to build predictive engines for diverse machine learning applications. It empowers users to swiftly create and launch an engine as a web service in a production environment using easily customizable templates. Upon deployment, it can handle dynamic queries in real-time, allowing for systematic evaluation and tuning of various engine models, while also enabling the integration of data from multiple sources for extensive predictive analytics. By streamlining the machine learning modeling process with structured methodologies and established evaluation metrics, it supports numerous data processing libraries, including Spark MLLib and OpenNLP. Users can also implement their own machine learning algorithms and integrate them effortlessly into the engine. Additionally, it simplifies the management of data infrastructure, catering to a wide range of analytics needs. Apache PredictionIO® can be installed as a complete machine learning stack, which includes components such as Apache Spark, MLlib, HBase, and Akka HTTP, providing a comprehensive solution for predictive modeling. This versatile platform effectively enhances the ability to leverage machine learning across various industries and applications.

Description

Deequ is an innovative library that extends Apache Spark to create "unit tests for data," aiming to assess the quality of extensive datasets. We welcome any feedback and contributions from users. The library requires Java 8 for operation. It is important to note that Deequ version 2.x is compatible exclusively with Spark 3.1, and the two are interdependent. For those using earlier versions of Spark, the Deequ 1.x version should be utilized, which is maintained in the legacy-spark-3.0 branch. Additionally, we offer legacy releases that work with Apache Spark versions ranging from 2.2.x to 3.0.x. The Spark releases 2.2.x and 2.3.x are built on Scala 2.11, while the 2.4.x, 3.0.x, and 3.1.x releases require Scala 2.12. The primary goal of Deequ is to perform "unit-testing" on data to identify potential issues early on, ensuring that errors are caught before the data reaches consuming systems or machine learning models. In the sections that follow, we will provide a simple example to demonstrate the fundamental functionalities of our library, highlighting its ease of use and effectiveness in maintaining data integrity.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
AWS Marketplace Yes 
Apache HBase Yes 
Apache Hadoop YARN Yes 
Docker Yes 
Elasticsearch Yes 
Java Yes 
MySQL Yes 
PHP Yes 
PostgreSQL Yes 
Python Yes 
Ruby Yes 
Scala Yes 

Integrations

Apache Spark Yes 
AWS Marketplace No 
Apache HBase No 
Apache Hadoop YARN No 
Docker No 
Elasticsearch No 
Java No 
MySQL No 
PHP No 
PostgreSQL No 
Python No 
Ruby No 
Scala No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Apache

Country

United States

Website

predictionio.apache.org

Vendor Details

Company Name

Deequ

Website

github.com/awslabs/deequ

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

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