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What is Fairly?

Effective risk management and oversight are essential for both AI and non-AI models to achieve optimal performance. Fairly provides a comprehensive continuous monitoring system that is tailored for strong model governance and oversight. This platform enhances collaboration among risk and compliance teams, as well as data science and cybersecurity experts, thereby ensuring that models uphold reliability and security standards. By offering a user-friendly approach, Fairly helps organizations stay updated with policies and regulations surrounding the procurement, validation, and auditing of non-AI, predictive AI, and generative AI models. The process of model validation and auditing is made more efficient through Fairly, which offers direct access to verified data in a controlled setting for both in-house and external models, alleviating extra pressures on development and IT teams. This capability ensures that Fairly's platform not only emphasizes compliance but also encourages secure and ethical modeling practices. Additionally, Fairly equips teams to proficiently identify, evaluate, and track risks, while also addressing and mitigating compliance, operational, and model-related risks in accordance with internal guidelines and external standards. Incorporating these functionalities, Fairly solidifies its dedication to upholding high levels of model integrity and accountability, ultimately contributing to a more reliable and responsible modeling landscape. Thus, organizations can trust that their models will not only meet regulatory requirements but also operate with ethical precision.

What is Datatron?

Datatron offers a suite of tools and features designed from the ground up to facilitate the practical implementation of machine learning in production environments. Many teams discover that deploying models involves more complexity than simply executing manual tasks. With Datatron, you gain access to a unified platform that oversees all your machine learning, artificial intelligence, and data science models in a production setting. Our solution allows you to automate, optimize, and expedite the production of your machine learning models, ensuring they operate seamlessly and effectively. Data scientists can leverage various frameworks to develop optimal models, as we support any framework you choose to utilize, including TensorFlow, H2O, Scikit-Learn, and SAS. You can easily browse through models uploaded by your data scientists, all accessible from a centralized repository. Within just a few clicks, you can establish scalable model deployments, and you have the flexibility to deploy models using any programming language or framework of your choice. This capability enhances your model performance, leading to more informed and strategic decision-making. By streamlining the process of model deployment, Datatron empowers teams to focus on innovation and results.

Media

Media

Integrations Supported

Microsoft Azure

Integrations Supported

Microsoft Azure
Amazon Web Services (AWS)
Google Cloud Platform
H2O.ai
Hadoop
MySQL
PostgreSQL
SAS Asset Performance Analytics
Salesforce
TensorFlow
Teradata VantageCloud
Wipro Promax

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Fairly

Company Location

Canada

Company Website

www.fairly.ai/

Company Facts

Organization Name

Datatron

Date Founded

2016

Company Location

United States

Company Website

www.datatron.com/platform/

Categories and Features

AI Governance

Not specified

Model Risk Management

Not specified

Categories and Features

AI Governance

Not specified

Machine Learning

Not specified

Model Risk Management

Not specified

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