Teradata Solves Biggest Challenges of Executing Analytics at Scale for Enterprise Customers
Teradata, the connected multi-cloud data platform for enterprise analytics company, announced that Vantage is now able to operationalize externally created predictive models, also known as model sharing or BYOM. This move further supports Teradata’s strategic analytics framework that gives data-driven enterprises a step-by-step solution for deploying analytical models at scale. Businesses will now be able to quickly realize a greater return on investment (ROI) in developing analytical models through increased model operationalization, expanded analytic use cases, and a streamlined approach to data-driven decision-making.
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Business leaders recognize that artificial intelligence and machine learning are the basis of competitive advantage in their industry, leading to an explosion in AI/ML technology investments. Despite these investments, many businesses are struggling to see returns from AI/ML projects due to inefficient data processes. To address this challenge, Teradata has created a strategic analytics framework – Analytics 1-2-3 – to establish a straightforward roadmap for businesses to create robust, efficient, and easily deployed processes that ensure AI/ML projects live up to their promise and deliver business value.
BYOM further supports this framework as it enables a significantly wider group of models and analytic algorithms to be available for deployment at scale. This means that models typically created on small systems with limited data sets can now be operationalized and scaled to the level required to score the various models rapidly, securely and consistently, all within Vantage.
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By leveraging this new functionality, Analytics 1-2-3 provides Teradata customers with an easy way to create and operationalize any number of models on any data volume in near real time.
- BYOM ensures customers can retain their investments in model development technologies without any risk or functionality loss when deployed in Vantage. This is realized by importing externally created predictive models by open-source packages or third-party solutions into Vantage, and then allowing the scoring of these models in parallel, using all the data that Vantage can ingest.
- As part of BYOM, Teradata data scientists can use any of their preferred open-source tools, such as R, Python, Apache Spark, SAS, KNIME, and more, to be executed in parallel alongside native Vantage analytic functions, enabling the operationalization of insights without needing to sample data or create data silos outside of Vantage.
- Teradata has a robust partnership community with leading advanced analytics and AI/ML vendors. The Analytics 1-2-3 framework naturally incorporates existing and new partners in Teradata’s analytics and AI/ML portfolio, providing customers, and the data science community, with the industry’s most optimal analytics ecosystem. Teradata provides customers support for their analytic tools of choice – now, in an industry-best, optimized configuration. The result is an analytics ecosystem that provides business users with answers and insights in minutes rather than hours or days, enables more robust models for deeper insights, and delivers faster model refresh, updates, and replacements.
“As our enterprise customers continue to explore the possibilities of AI to increase customer engagement, revenue, and reduce risk and cost, they need solutions that are built for the complexity of today’s modern data analytic ecosystem,” said Hillary Ashton, Chief Product Officer at Teradata. “Teradata Vantage was built with the flexibility and scalability to handle the most complex enterprise workloads, regardless of where the data sits. Now, with its new BYOM functionality, Vantage can address the most stubborn challenges facing organizations that wish to quickly realize value from their AI/ML investments.”
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