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CertificationHyperSense AI
Operationalize
Deploy and operationalize ML models within days. Access suite of MLOps capabilities to deploy, manage and re-learn models without needing expert machine learning engineers
HyperSense AI
Workflow
Scale and operationalize models built in HyperSense or outside in one place using end-to-end machine learning operations (MLOps). You can deploy ML models with data pipelines in a few clicks without dedicating separate resources to manage and maintain models. Models can also be configured for scheduled event-based maintenance.
Features
Operationalize
Deployment suitable for your business needs
- Deploy both internal and external models in batch & real-time. Integrate models into external applications using RESTFUL APIs.
Operationalize
Get explainable predictions quickly
- Get explanations for individual predictions with prescriptive insights. Also, flag prediction scenarios for retraining, exclusions, or further investigation.
Operationalize
Automated Model Lifecycle Management
- Monitor models in production for data quality, model quality, bias detection, trustworthiness, etc.
- Stage challenger models in a staging environment for testing before going live
- Deploy models in several champion-challenger modes, such as canary, flight, etc.
Operationalize
Embedded with Governance
- Enable the governance process for AI projects across the organization.
- Ensures transparency by creating better models with minimal bias and tracking version history and model origin, which helps to meet governance requirements.
Operationalize
Model version and lineage
Track model version history and model origin to track changes made to the models, which helps to meet regulatory compliance and model reproducibility.
Operationalize
Achieve Collaboration
Access to custom dashboards, auto reports, and web applications enables IT and business teams to monitor, control, and enhance model performance.
Benefits
Reduced Time-to-Market
Easy and quick deployment of high precision machine learning and artificial inteligence models reduces time-to-market of AI applications.
Increase Efficiency
Automate model management with MLOps and allow data scientists to focus on higher-value tasks such as more in-depth business expertise, etc.
Derive quicker AI value
Derive real business value from AI applications with intelligent ML models that can predict, classify, and cluster huge complex enterprise data.
Foster Innovation
Quickly and easily delivers accurate, advanced ML models with the help of custom apps and a machine learning lifecycle management that fosters innovation.
Improve decision making
Create complex business rules using a drag-and-drop builder with No-Code AI platform and improve data-driven decision-making throughout the organization.
Enabled Collaboration
Collaborate with all the stakeholders on a single platform to share and manage model assets, track success and improve the models.
