HyperSense AI monitors ML models for any changes and maintains an acceptable level of performance. It also monitors alerts in real-time and notifies the specific user group to take action on model health regularly. It also seeks model explanations to understand model behavior and decisions through Explainable AI capabilities.
HyperSense AI allows to build and deploy ML models in a production environment easily. It monitors and manages the entire machine learning model lifecycle and ensures all the stages of the machine learning model lifecycle act and interact consistently and send alerts in case of any issues.
HyperSense AI offers all three deployment modes: Batch, Real-time, and Rest API Integration. It aligns with business operations and provides the ability to integrate with both internal and external applications.
HyperSense AI supports various ML Deployment Strategies such as Rolling, Canary, and Champion-Challenger. It ensures models’ best performance is delivered all the time to the end consumers and sustains user experience.
HyperSense AI provides a robust way to store and access all details of AI models. This is achieved using full pipeline serving, Staging vs. Production, Auto Report Generation, and review & approval process. It deploys models in an agile manner and access auto model reports and compliance reports to support real-time decisions on the models.
HyperSense AI monitors ML models for any changes and maintains an acceptable level of performance. This happens using drift analysis, model quality, bias detection, vulnerability assessment on adversarial attacks, and business performance tracking. It also monitors alerts in real-time and notifies the specific user group to take action on model health regularly. It also seeks model explanations to understand model behavior and decisions through Explainable AI capabilities. It monitors both White-Box and Black-Box attacks and covers the assessment of those threat types.
HyperSense AI helps maintain the model by identifying and mitigating the risks that ML models experience once they are deployed in the production environment using auto retraining, bias mitigation, and recommendation on model robustness against adversarial attacks.
It helps make better and timely decisions through a model management process that organizes, evaluates, and provides orderly information.
Enables seamless collaboration amongst all stakeholders by providing a centralized place to communicate and access various insights instantly.
It helps establish and allocate more critical and dependent tasks to each other quickly and efficiently according to their relevant skills.
© Copyright SUBEX 2023. All Rights Reserved.
Business Assurance
Campaign Management
Churn Prediction
Fraud Management
Product Recommendation
Telecom
Fintech
E-commerce
Banking
Financial Services
Insurance
Blogs
AI Glossary
AI Resource Center
AI Tutorials
Media Articles
Certification