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Teradata Enhances ClearScape Analytics with New Flexible, Scalable, and Intuitive ModelOps for Agentic and Generative AI
New capabilities ease integration and deployment just as organizations move from AI experimentation to trusted enterprise-scale production
Teradata (NYSE: TDC) today announced ModelOps updates to ClearScape Analytics, streamlining the path from research to production for Agentic AI and Generative AI use cases.
Teradata’s new unified ModelOps platform is designed to provide analytics leaders and data scientists with seamless, native support for open-source ONNX embedding models as well as cloud service provider LLM APIs - such as Azure OpenAI, Amazon Bedrock, and Google Gemini. These models can be deployed, managed, and monitored without custom development thanks to newly enhanced LLMOps capabilities. ModelOps also empowers business analysts and non-technical users with low-code AutoML capabilities and delivers a consistent intuitive interface across all tools. This upgrade is designed to eliminate the complexity of managing disparate AI systems while democratizing use across skill levels, enabling organizations to scale their AI operations more efficiently with reduced onboarding time and improved productivity.
As organizations transition from AI experimentation to production scale, they encounter critical challenges that prevent meaningful business impact. Fragmented workflows across different LLM providers — including the growing number of open-source models that organizations are adopting — create limited model interoperability and steep learning curves that hinder adoption and slow innovation. Without unified governance frameworks, organizations struggle to establish reliability and compliance across AI systems, making it impossible to scale trusted AI with confidence. These limitations can force generative AI and agentic AI initiatives to remain isolated experiments rather than integrated business solutions, hindering value creation.
Teradata’s new ModelOps platform is designed to solve these challenges by providing unified access to diverse AI models and low-code tools, while maintaining trust and governance at scale, eliminating the operational complexity that prevents business users from realizing AI's full potential.
“The reality is that organizations will use multiple AI models and providers — it's not a question of if, but how, to manage that complexity effectively,” said Sumeet Arora, Teradata’s Chief Product Officer. “Teradata’s ModelOps offering provides the flexibility to work across combinations of models while maintaining trust and governance. Companies can then move confidently from experimentation to production, at scale, realizing the full potential of their AI investments."
An ideal use case for Teradata’s new ModelOps offering might be that a bank needs to understand customer satisfaction and identify service issues across multiple feedback channels to improve their digital banking experience and retain customers. Rather than managing separate AI tools for different analysis tasks, the unified ModelOps platform allows the bank to seamlessly combine multiple AI models (using LLMs for sentiment analysis, embedding models for categorization, and AutoML for predictive insights, for example) all within a single, trusted environment. This integrated approach enables both technical teams and business analysts to move quickly from experimental analysis to production-scale customer intelligence that directly impacts retention and satisfaction.
Key Features and Expected Benefits of the New ModelOps in ClearScape Analytics:
- Seamless Integration with Public LLM APIs:
- Enables organizations to easily connect with top AI providers — Azure OpenAI, Google Gemini, and Amazon Bedrock for popular LLMs such as Anthropic, Mistral, DeepSeek, and Meta — all without custom development, enhancing flexibility and model choice.
- Securely register and manage external API-based models with observability, autoscaling, and usage analytics.
- Admins can configure retry policies, concurrency, and replica settings, while tracking health and spend per project or model.
- Managing and monitoring LLMs with LLMOps:
- Enables rapid deployment of NVIDIA NIM LLMs directly into GPU environments with streamlined, configuration-free workflows.
- Provides comprehensive LLM Model Cards that deliver transparency and monitoring with built-in governance capabilities.
- Delivers complete end-to-end lifecycle management for LLMs including deployment, versioning, performance tracking, and retirement processes.
- Streamlined ONNX Embedding Model Deployment:
- Offers native support for ONNX embedding models and tokenizers in a unified workflow, simplifying deployment of custom vector search models and reducing engineering overhead.
- Bring-Your-Own-Model workflows are also fully supported.
- Low-Code AutoML:
- Empowers teams to build and monitor models using intuitive tools that support scheduling, performance monitoring, and visual explainability, reducing reliance on ML specialists.
- Business analysts and data scientists can use a low-code interface for model training, evaluation, and deployment. Integrated telemetry and explainability tools provide transparency and trust.
- Consistent, Intuitive User Interface:
- Delivers a unified UX across AutoML, Playground, Tables, and Datasets, lowering the learning curve and improving productivity for AI teams of all skill levels.
- From guided wizards to improved table interactions and a more powerful AI Workbench Playground, ModelOps delivers a cohesive and user-friendly experience.
Availability
This upgraded version of ModelOps is expected to be available in Q4 for AI Factory and VantageCloud.