HyperSense AI
Model Management

HyperSense AI Model Management is a central hub to deploy, monitor, manage, and govern all the models in production to establish clear practices to ensure consistent management and minimize risks.

Manage the entire ML lifecycle in one place

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.

Model Management

Deployment Modes

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.

Deployment Strategies

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.

Model Registry

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.

Model Monitoring

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 regularly take action on model health. It also seeks model explanations to understand model decisions.

Model Maintenance

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.

Benefits of Model Management

Improves Decision Making

It helps make better and timely decisions through a model management process that organizes, evaluates, and provides orderly information.

Better Collaboration

Enables seamless collaboration amongst all stakeholders by providing a centralized place to communicate and access various insights instantly.

Quick and Easy Planning

It helps establish and allocate more critical and dependent tasks to each other quickly and efficiently according to their relevant skills.

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