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Description
Examine the causes of failures right after the testing phase concludes. Create straightforward and easy-to-read reports for your teams. Utilize machine learning-driven auto-analyzers to delve into the reasons behind the failures. Consolidate test outcomes from different platforms, frameworks, and programming languages while delivering actionable insights. Employing machine learning algorithms helps to uncover patterns in the test data, identify the underlying causes of failures, and forecast future testing outcomes. Support the manual examination of test logs and emerging failure patterns from the latest test runs. Enable automated decision-making processes for release pipelines by adhering to defined testing criteria and outcomes. Present test results in a clear format that facilitates monitoring of trends, recognition of patterns, generation of insights, and informed business choices. Regularly assess your product's health and automate release decisions with Quality Gates to enhance efficiency and reliability. This approach not only streamlines the testing process but also significantly contributes to improving overall product quality.
Description
The Robust Intelligence Platform is designed to integrate effortlessly into your machine learning lifecycle, thereby mitigating the risk of model failures. It identifies vulnerabilities within your model, blocks erroneous data from infiltrating your AI system, and uncovers statistical issues such as data drift. Central to our testing methodology is a singular test that assesses the resilience of your model against specific types of production failures. Stress Testing performs hundreds of these evaluations to gauge the readiness of the model for production deployment. The insights gained from these tests enable the automatic configuration of a tailored AI Firewall, which safeguards the model from particular failure risks that it may face. Additionally, Continuous Testing operates during production to execute these tests, offering automated root cause analysis that is driven by the underlying factors of any test failure. By utilizing all three components of the Robust Intelligence Platform in tandem, you can maintain the integrity of your machine learning processes, ensuring optimal performance and reliability. This holistic approach not only enhances model robustness but also fosters a proactive stance in managing potential issues before they escalate.
API Access
Has API
API Access
Has API
Integrations
Active Directory
Amazon Redshift
Azure DevOps
DataRobot
GitHub
GitLab
Google Cloud BigQuery
Google Cloud Storage
H2O.ai
IBM watsonx.data
Integrations
Active Directory
Amazon Redshift
Azure DevOps
DataRobot
GitHub
GitLab
Google Cloud BigQuery
Google Cloud Storage
H2O.ai
IBM watsonx.data
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
ReportPortal
Founded
2013
Country
United States
Website
reportportal.io
Vendor Details
Company Name
Robust Intelligence
Founded
2019
Country
United States
Website
www.robustintelligence.com
Product Features
Automated Testing
Hierarchical View
Move & Copy
Parameterized Testing
Requirements-Based Testing
Security Testing
Supports Parallel Execution
Test Script Reviews
Unicode Compliance
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization