TLDR: Teradata has announced significant updates to its ClearScape Analytics platform, introducing enhanced ModelOps capabilities specifically designed to streamline the deployment and management of generative and agentic AI models in enterprise environments. These updates aim to simplify complex AI operations, provide unified governance, and accelerate the transition from AI experimentation to large-scale production.
SAN DIEGO – July 29, 2025 – Teradata Corp. (NYSE: TDC) today unveiled substantial enhancements to its ClearScape Analytics platform, focusing on robust ModelOps capabilities to support the burgeoning demand for generative and agentic AI deployments within enterprises. The updates are engineered to bridge the gap between AI research and production, offering a more streamlined and efficient pathway for organizations.
The new unified ModelOps platform provides seamless, native support for open-source ONNX embedding models and integrates with leading cloud service provider Large Language Model (LLM) APIs, including Azure OpenAI, Amazon Bedrock, and Google Gemini. This broad compatibility allows for the deployment, management, and monitoring of diverse AI models without the need for extensive custom development, thanks to newly enhanced LLMOps capabilities.
Sumeet Arora, Teradata’s Chief Product Officer, emphasized the evolving landscape of AI adoption, stating, “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.” He added, “Teradata’s ModelOps offering provides the flexibility to work across combinations of models while maintaining trust and governance.” This flexibility is crucial as businesses navigate the complexities of integrating various AI solutions.
Beyond technical users, the updated ModelOps platform also empowers business analysts and non-technical users with low-code AutoML capabilities, delivering a consistent and intuitive interface across all tools. This approach is designed to democratize AI use across different skill levels, reducing onboarding time and improving overall productivity by eliminating the complexities associated with managing disparate AI systems.
Organizations often face critical challenges when scaling AI from experimental phases to full production, including fragmented workflows, limited model interoperability, and a lack of unified governance. Teradata’s new ModelOps platform directly addresses these issues by providing unified access to diverse AI models and low-code tools, ensuring trust and governance at scale. This helps prevent generative and agentic AI initiatives from remaining isolated experiments, instead enabling them to become integrated business solutions that drive significant value.
Key features of the enhanced platform include seamless integration with public LLM APIs from major cloud providers, comprehensive LLMOps capabilities for managing and monitoring large language models, and support for NVIDIA NIM LLMs with GPU deployment. The platform also facilitates ONNX embedding model deployment and offers low-code AutoML tools for model building and monitoring. Administrative features encompass configuration of retry policies, concurrency settings, and usage analytics tracking.
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The upgraded ModelOps version is anticipated to be available in the fourth quarter of 2025 for Teradata’s AI Factory and VantageCloud platforms, marking a significant step towards more accessible and reliable enterprise AI solutions.
