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Deploy More Models Faster
In general, more than 50% of data science and machine learning models never make it to production business systems. TIBCO Model Ops 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. Model Ops 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.