Deploy More Models Faster
In general, more than 50% of data science and machine learning models never make it to production business systems. TIBCO® ModelOps software automates and simplifies the deployment and monitoring process so you can start using more models faster, delivering real business value with AI-infused decisioning.
Collaborate & Accelerate Time to Value
To make usable, deployable models, you need a good team of data engineers, data scientists, and devops specialists working closely together to ensure that models meet business requirements. TIBCO offers a collaborative environment that helps reduce the friction between data science and IT. Eliminate re-coding of models and deliver the models to IT in the right format required for production.
Deploy Your Models Your Way
Use virtually any open source or cloud-based AI model and flexibly manage thousands of statistical, machine learning (ML), artificial intelligence (AI), statistical, and rules-based models. Efficiently deploy them into production from anywhere, in virtually any format, whether coded as programs or scripts or created using standard formats or available as microservice endpoints in a cloud environment.
Govern the AI/ML model lifecycle to mitigate risks of algorithmic decisions and AI-guided customer interaction. You'll have the transparency to determine whether model decisions are justified, understood, traced, and doing no harm. ModelOps software is essential for people-facing applications such as credit & insurance risk assessment and pricing, medical decisions, fraud & intrusion detection, and more.
Monitor from Customizable Dashboards
Empower stakeholders throughout the enterprise to effectively monitor model performance and impact using state-of-the-art visualizations in TIBCO Spotfire® software or third-party visualization tool. Use statistical metrics to monitor accuracy and population stability, and performance and business metrics to track ROI.
Reuse Models from a Central Repository
Stop reinventing the wheel and work smarter. Reuse and repurpose models from a centrally managed repository to accelerate productivity. Reduce duplicated effort and decongest the ML pipeline by searching the repository for appropriate models and track their use in projects.