Best Machine Learning Software for DataRobot

Mona is a flexible and intelligent monitoring platform for AI / ML. Data science teams leverage Mona’s powerful analytical engine to gain granular insights about the behavior of their data and models, and detect issues within specific segments of data, in order to reduce business risk and pinpoint areas that need improvements. Mona enables tracking custom metrics for any AI use case within any industry and easily integrates with existing tech stacks. In 2018, we ventured on a mission to empower data teams to make AI more impactful and reliable, and to raise the collective confidence of business and technology leaders in their ability to make the most out of AI. We have built the leading intelligent monitoring platform to provide data and AI teams with continuous insights to help them reduce risks, optimize their operations, and ultimately build more valuable AI systems. Enterprises in a variety of industries leverage Mona for NLP/NLU, speech, computer vision, and machine learning use cases. Mona was founded by experienced product leaders from Google and McKinsey&Co, is backed by top VCs, and is HQ in Atlanta, Georgia. In 2021, Mona was recognized by Gartner as a Cool Vendor in AI Operationalization and Engineering.

You can now build what took years. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy and maintain ML in production. Superwise integrates with any ML stack, and can connect to any number of communication tools. Want to go further? Superwise is API-first. All of our APIs allow you to access everything, and we mean everything. All this from the comfort of your cloud. You have complete control over ML monitoring. You can set up metrics and policies using our SDK and APIs. Or, you can simply choose a template to monitor and adjust the sensitivity, conditions and alert channels. Get Superwise or contact us for more information. Superwise's ML monitoring policy templates allow you to quickly create alerts. You can choose from dozens pre-built monitors, ranging from data drift and equal opportunity, or you can customize policies to include your domain expertise.

This machine learning monitoring tool allows you to easily monitor and troubleshoot large model volumes. Data scientists can avoid false alarms and dead ends by using an unrivaled explainability accuracy and unique analyses that aren't available anywhere else. This allows them to quickly and effectively address critical problems. So that your business runs at its best, machine learning models are optimized. TruEra's explainability engine is the result of years of dedicated research and development. It is significantly more accurate that current tools. TruEra's enterprise-class AI explainability tech is unrivalled. The core diagnostic engine is built on six years of research by Carnegie Mellon University. It outperforms all competitors. The platform performs sophisticated sensitivity analyses quickly, allowing data scientists, business users, risk and compliance teams to understand how and why a model makes predictions.

To develop AI solutions, engineers must have a resilient, open, repeatable engineering approach to ensure quality and agility. These efforts have not been able to address the challenges of today's complex environment, which is filled with a variety of tools and rapidly changing data. Platform for collaborative development that automates the control and development of AI applications across multiple persons. To predict customer behavior in real-time, and at scale, we can derive hyper-detailed customer profiles using enterprise data. AI-powered models that can continuously learn and achieve clearly defined business results. Allows organizations to demonstrate compliance with applicable rules and regulations. CognitiveScale's Cortex AI Platform is designed to address enterprise AI use cases using modular platform offerings. Customers use and leverage its capabilities in microservices as part of their enterprise AI initiatives.

Robust Intelligence Platform seamlessly integrates into your ML lifecycle to eliminate any model failures. The platform detects weaknesses in your model, detects statistical data issues such as drift, and prevents data from being inserted into your AI system. A single test is the heart of our test-based approach. Each test measures the model's resistance to a particular type of production model failure. Stress Testing runs hundreds upon hundreds of these tests in order to assess model production readiness. These tests are used to automatically configure an AI Firewall to protect the model from the specific types of failures to which it is most vulnerable. Continuous Testing also runs these tests during production. Continuous Testing provides an automated root cause analysis that identifies the root cause of any test failure. ML Integrity can be ensured by using all three elements of Robust Intelligence.