Teradata announced it will offer Teradata VantageCloud Lake on Microsoft Azure, opening up new markets, new capabilities, and new use cases. This makes Teradata’s modern cloud-native architecture, and robust analytics capabilities in ClearScape Analytics, available to customers interested in using harmonized data and trusted AI to drive faster innovation.
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“Generative AI and large language models (LLMs) are set to fundamentally transform every industry, dramatically amplifying what people can achieve”
ClearScape Analytics, the powerful, open, and connected analytics framework that is included with VantageCloud Lake on Azure, is enhanced by integration with Microsoft services such as Azure Machine Learning (ML). This means VantageCloud Lake on Azure is designed to dramatically increase the ability of mutual customers to deploy and manage AI/ML, including generative AI, within their businesses today.
The advent of generative AI has ignited a race to use the technology in organizations of all sizes. IDC recently surveyed CIOs, CDOs, and CFOs, and found that more than half expect significant pressure to use generative AI capabilities in the next six to twelve months. In fact, nearly 95% expect to feel at least some pressure to implement and/or use generative AI.
“Generative AI and large language models (LLMs) are set to fundamentally transform every industry, dramatically amplifying what people can achieve,” said Hillary Ashton, Chief Product Officer at Teradata. “Teradata and Microsoft are among the leading companies with the expertise and innovative technology to help organizations get ahead of this opportunity and make significant improvements across business performance and customer experiences. VantageCloud Lake on Azure provides the backbone to deliver trusted AI and ML across the entire enterprise.”
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VantageCloud Lake on Azure
With the availability of VantageCloud Lake on Azure, Teradata and Microsoft customers will benefit from modern cloud-native architecture, including the full separation of compute and storage, as well as independent, elastic, and multi-cluster compute – all against Azure Data Lake Storage. This cost-effective, dynamically scalable solution with self-service provisioning enables Azure customers to execute a wide array of analytic workloads (including AI/ML) at scale, with harmonized data across the enterprise.
Customers can take advantage of Teradata’s new cloud-native architecture to more easily modernize their workloads and applications, at their own pace. This process minimizes risk and accelerates time-to-value with the fastest, most efficient cloud migration to Azure.
VantageCloud Lake on Azure customers also have access to an exclusive high availability capability that uses a key Azure feature to increase cloud availability. By offering optimized system sizes in the new offering, customers can see increases in overall uptime.
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Robust Analytics for AI, ML and Generative AI
Enterprises face major hurdles in driving innovation with new AI/ML use cases. One of the biggest is delivering impactful outcomes, when moving analytics from POCs to production takes too long or doesn’t happen at all. To overcome these challenges, Teradata’s ClearScape Analytics and Microsoft Azure ML offer one of the most robust analytic capabilities for end-to-end analytic pipelines, including data preparation, model training, and operationalizing at scale. Businesses can achieve better results using data from VantageCloud Lake in conjunction with the power of ClearScape Analytics and Azure ML – not only for activating analytics across the enterprise, but also in new generative AI use cases.
“We’re combining Teradata VantageCloud and ClearScape Analytics with Azure’s machine learning ability to simplify and accelerate the ML lifecycle,” said Erin Chapple, Corporate Vice President, Azure Core Product and Design at Microsoft Corp. “The solutions ensure customers can execute complex analytics on massive data sets and incorporate their preferred data science tools like Azure ML. This helps customers unlock the potential of their AI/ML investments as they deploy sophisticated models, including generative models, in production, faster and with confidence.”
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