Latest news with #ClearScapeAnalytics
Techday NZ
30-07-2025
- Business
- Techday NZ
Teradata upgrades ModelOps for scalable enterprise AI use
Teradata has introduced ModelOps updates to its ClearScape Analytics offering, targeting streamlined integration and deployment for Agentic AI and Generative AI applications as organisations transition from experimentation to production at scale. ModelOps platform The updated ModelOps platform aims to support analytics professionals and data scientists with native compatibility for open-source ONNX embedding models and leading cloud service provider large language model (LLM) APIs, including Azure OpenAI, Amazon Bedrock, and Google Gemini. With these enhancements, organisations can deploy, manage, and monitor AI models without having to rely on custom development, with newly added LLMOps capabilities designed to simplify workflows. For less technical users such as business analysts, ModelOps also integrates low-code AutoML tools, providing an interface that facilitates intuitive access for users of different skill levels. The platform's unified interface is intended to reduce onboarding time and increase productivity by offering consistent interactions across its entire range of tools. Challenges in AI adoption Many organisations encounter challenges when progressing from AI experimentation to enterprise-wide implementation. According to Teradata, the use of multiple LLM providers and the adoption of various open-source models can cause workflow fragmentation, limited interoperability, and steep learning curves, ultimately inhibiting wider adoption and slowing down innovation. Unified governance frameworks are often lacking, making it difficult for organisations to maintain reliability and compliance requirements as they scale their AI capabilities. These issues may cause generative and agentic AI projects to remain in isolation, rather than delivering integrated business insights. As a result, organisations could lose value if they are unable to effectively scale AI initiatives due to operational complexity and fragmented systems. Unified access and governance "The reality is that organisations will use multiple AI models and providers - it's not a question of if, but how, to manage that complexity effectively. 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, realising the full potential of their AI investments," said Sumeet Arora, Teradata's Chief Product Officer. Teradata's ModelOps strategy is designed to provide unified access to a range of AI models and workflows, while maintaining governance and ease of use. This is intended to allow business users to deploy AI models quickly and safely, supporting both experimentation and production use. An example scenario described by Teradata involved a bank seeking to improve its digital customer experience and retention rates by analysing customer feedback across channels. The unified ModelOps platform would allow the bank to consolidate multiple AI models - such as LLMs for sentiment analysis, embedding models for categorisation, and AutoML for predictive analytics - within one environment. The aim is to equip both technical and non-technical teams to act on customer intelligence at greater speed and scale. Key features The updated ModelOps capabilities in ClearScape Analytics include: Seamless Integration with Public LLM APIs : Users can connect with APIs from providers such as Azure OpenAI, Google Gemini, and Amazon Bedrock for a variety of LLMs, including Anthropic, Mistral, DeepSeek, and Meta. This integration supports secure registration, monitoring, observability, autoscaling, and usage analytics. Administrative options are available for retry policies, concurrency, and health or spend tracking at the project or model level. : Users can connect with APIs from providers such as Azure OpenAI, Google Gemini, and Amazon Bedrock for a variety of LLMs, including Anthropic, Mistral, DeepSeek, and Meta. This integration supports secure registration, monitoring, observability, autoscaling, and usage analytics. Administrative options are available for retry policies, concurrency, and health or spend tracking at the project or model level. Managing and monitoring LLMs with LLMOps : The platform supports rapid deployment of NVIDIA NIM LLMs within GPU environments. Features include LLM Model Cards for transparency, monitoring, and governance, as well as full lifecycle management - covering deployment, versioning, performance tracking, and retirement. : The platform supports rapid deployment of NVIDIA NIM LLMs within GPU environments. Features include LLM Model Cards for transparency, monitoring, and governance, as well as full lifecycle management - covering deployment, versioning, performance tracking, and retirement. ONNX Embedding Model Deployment : ClearScape Analytics natively supports ONNX embedding models and tokenisers, including support for Bring-Your-Own-Model workflows and unified deployment processes for custom vector search models. : ClearScape Analytics natively supports ONNX embedding models and tokenisers, including support for Bring-Your-Own-Model workflows and unified deployment processes for custom vector search models. Low-Code AutoML : Teams can create, train, monitor, and deploy models through an accessible low-code interface with performance monitoring and visual explainability features. : Teams can create, train, monitor, and deploy models through an accessible low-code interface with performance monitoring and visual explainability features. User Interface Improvements: The upgrade provides a unified user experience across all major tools, such as AutoML, Playground, Tables, and Datasets, with guided wizards and new table interaction options aimed at reducing skill barriers. Availability of the updated ModelOps in ClearScape Analytics is anticipated in the fourth quarter for users of AI Factory and VantageCloud platforms. Follow us on: Share on:
Techday NZ
15-07-2025
- Business
- Techday NZ
Teradata VantageCloud delivers 427% ROI & cuts costs with AI
Organisations using Teradata VantageCloud have recorded an average return on investment of 427% over three years, along with a typical payback period of 11 months, according to a new study by Nucleus Research. Study findings The independent analysis by Nucleus Research assessed the financial and operational impacts of Teradata VantageCloud amongst companies across sectors including healthcare, insurance, and telecommunications, with employee counts from 5,000 to 45,000. Results show that, on average, adopters saw annual benefits totalling USD $7.9 million, alongside the substantial ROI and rapid payback time. Alex Wurm, Senior Analyst at Nucleus Research, said, "The financial results demonstrate that enterprises can achieve substantial returns while modernising their data infrastructure. Teradata's innovative approach combines cloud with hybrid flexibility and addresses the real-world constraints organisations face, delivering both immediate operational benefits and long-term strategic value." The study highlights that cost reductions and operational efficiencies played a significant role in the financial advantage reported by respondents. For example, a telecommunications company was able to avoid the expense of a seven-year infrastructure upgrade, saving USD $350,000 annually through the use of the Teradata platform. The company also benefited from a 43% reduction in administrative overhead by moving to a fully managed service model, providing annual direct cost savings of USD $735,000. Operational improvements Performance enhancements also contributed to the results. According to Nucleus Research, the study found improvements in data processing performance ranging from 25% to 30%, while backup operations became 87% to 90% faster compared to traditional manual methods. These increases in automation and speed enabled IT teams to allocate more resources to strategic projects, rather than day-to-day administrative duties. Organisations making use of Teradata VantageCloud for artificial intelligence and machine learning reported faster model delivery, with speeds increasing by 26% to 75%. In some cases, model deployment times were reduced from over a month to just a week. The accuracy of AI models developed using the ClearScape Analytics platform by Teradata saw lifts of 10% to 15%. "By integrating Teradata's advanced analytics into our retention strategies, we're not just predicting customer churn – we're preventing it. After transitioning to Teradata's ClearScape Analytics, our AI-driven churn modeling saw a 10-15% lift in accuracy," noted a telecommunications provider. Real-time results Examples cited in the study include a healthcare provider with a patient base exceeding three million, which was able to reduce integration timelines by 67% by consolidating previously siloed systems. Elsewhere, a commercial insurer handling 200TB of data reported the ability to process a full day's financial transactions in just 10 minutes, a change attributed to real-time analytics that underpin quicker decision-making. Assessment methods Nucleus Research conducted detailed ROI assessments through interviews with Teradata VantageCloud customers. The analysis covered total costs and benefits, including software subscriptions, consulting, personnel changes, and operational impacts over the three-year evaluation period. Standard NASBA accounting principles were applied to ensure accuracy and credibility in the reporting process. For organisations seeking to calculate their potential return from adopting VantageCloud, Teradata now offers a business value assessment tool. The calculator, which uses Nucleus Research-verified data, aims to help companies gauge possible financial outcomes by entering details about their current operational environment and project objectives.
