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What is Rinalogy Classification API?

The Rinalogy Classification API presents a versatile machine learning solution that integrates effortlessly with your current application, enabling you to function within your own infrastructure. Unlike conventional cloud-based machine learning APIs that require data transfer and operate externally, Rinalogy can be deployed within your IT environment, thereby ensuring data security and compliance while operating behind your firewall. This API implements Exhaustive Sequential Classification, methodically applying models to each document in a dataset. The models produced can be refined with additional training data or used to forecast outcomes for new documents at a later stage. Its capacity to scale via cluster deployment allows you to adjust the number of workers to fit your existing workload requirements. Moreover, the Rinalogy API enhances client applications by integrating features such as text classification, improved search functionalities, and tailored recommendations, offering a robust toolkit for data-driven decision-making. This adaptability not only boosts efficiency but also empowers organizations to fine-tune their machine learning strategies while retaining oversight of their data, making it a highly attractive option for businesses looking to streamline their operations. Overall, the Rinalogy Classification API stands out as a comprehensive solution for those prioritizing flexibility and security in their machine learning endeavors.

What is ML.NET?

ML.NET is a flexible and open-source machine learning framework that is free and designed to work across various platforms, allowing .NET developers to build customized machine learning models utilizing C# or F# while staying within the .NET ecosystem. This framework supports an extensive array of machine learning applications, including classification, regression, clustering, anomaly detection, and recommendation systems. Furthermore, ML.NET offers seamless integration with other established machine learning frameworks such as TensorFlow and ONNX, enhancing the ability to perform advanced tasks like image classification and object detection. To facilitate user engagement, it provides intuitive tools such as Model Builder and the ML.NET CLI, which utilize Automated Machine Learning (AutoML) to simplify the development, training, and deployment of robust models. These cutting-edge tools automatically assess numerous algorithms and parameters to discover the most effective model for particular requirements. Additionally, ML.NET enables developers to tap into machine learning capabilities without needing deep expertise in the area, making it an accessible choice for many. This broadens the reach of machine learning, allowing more developers to innovate and create solutions that leverage data-driven insights.

Media

Media

Integrations Supported

.NET
Bing
C#
F#
Google Cloud AutoML
Microsoft Defender Antivirus
Microsoft Outlook
Microsoft Power BI
ONNX
TensorFlow

Integrations Supported

.NET
Bing
C#
F#
Google Cloud AutoML
Microsoft Defender Antivirus
Microsoft Outlook
Microsoft Power BI
ONNX
TensorFlow

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

RINA Systems

Company Location

United States

Company Website

www.rinasystems.com/product/rinalogy-api.html

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

dotnet.microsoft.com/en-us/apps/ai/ml-dotnet

Categories and Features

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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