About
Apache PredictionIO® is an open-source machine learning server built on top of a state-of-the-art open-source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you quickly build and deploy an engine as a web service on production with customizable templates. Respond to dynamic queries in real-time once deployed as a web service, evaluate and tune multiple engine variants systematically, and unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics. Speed up machine learning modeling with systematic processes and pre-built evaluation measures, support machine learning and data processing libraries such as Spark MLLib and OpenNLP. Implement your own machine learning models and seamlessly incorporate them into your engine. Simplify data infrastructure management. Apache PredictionIO® can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Akka HTTP, etc.
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About
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.
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About
The Java™ Programming Language is a general-purpose, concurrent, strongly typed, class-based object-oriented language. It is normally compiled to the bytecode instruction set and binary format defined in the Java Virtual Machine Specification. In the Java programming language, all source code is first written in plain text files ending with the .java extension. Those source files are then compiled into .class files by the javac compiler. A .class file does not contain code that is native to your processor; it instead contains bytecodes — the machine language of the Java Virtual Machine1 (Java VM). The java launcher tool then runs your application with an instance of the Java Virtual Machine.
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About
Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Developers and data scientists searching for a solution to create predictive engines
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Audience
Organizations that want a unified analytics engine for large-scale data processing
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Audience
Developers looking for a Programming Language solution
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Audience
Data scientists looking for a machine learning solution to accelerate the creation and deployment of machine learning models
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationApache
United States
predictionio.apache.org
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Company InformationApache Software Foundation
Founded: 1999
United States
spark.apache.org
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Company InformationOracle
docs.oracle.com/javase/8/docs/technotes/guides/language/index.html
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Company InformationOracle
Founded: 1977
United States
www.oracle.com/data-science/machine-learning/
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Alternatives |
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Categories |
Categories |
Categories |
Categories |
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Streaming Analytics Features
Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards
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Integrations
Apache APISIX
CI Fuzz
CodeSee
CodeSession
DeepSeek R2
Eclipse IDE
Eclipse Theia
Formatter V7
Java
Kinetica
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Integrations
Apache APISIX
CI Fuzz
CodeSee
CodeSession
DeepSeek R2
Eclipse IDE
Eclipse Theia
Formatter V7
Java
Kinetica
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Integrations
Apache APISIX
CI Fuzz
CodeSee
CodeSession
DeepSeek R2
Eclipse IDE
Eclipse Theia
Formatter V7
Java
Kinetica
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Integrations
Apache APISIX
CI Fuzz
CodeSee
CodeSession
DeepSeek R2
Eclipse IDE
Eclipse Theia
Formatter V7
Java
Kinetica
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