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Teradata Accelerates Enterprise Scalability of Artificial Intelligence and Machine Learning Projects in Teradata Vantage with Amazon SageMaker

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Teradata (TDC) has launched the integration of its Vantage multi-cloud data and analytics platform with Amazon SageMaker, enhancing advanced analytics access for organizations. This partnership addresses scalability challenges in AI/ML deployment, promising faster time-to-value and improved ROI through the Analytics 123 framework. Despite heavy investments in AI/ML, many organizations struggle with widespread deployment due to costly data preparation. Vantage aims to enable enterprise-scale performance, allowing rapid AI/ML project execution.

Positive
  • Integration of Teradata Vantage with Amazon SageMaker enhances advanced analytics capabilities.
  • The partnership aims to improve ROI and accelerate AI/ML project deployment.
  • Analytics 123 framework provides a scalable solution for AI/ML model deployment.
Negative
  • Many organizations still struggle with deploying AI/ML solutions widely.
  • Data preparation for AI/ML remains a costly and time-consuming process.

Introduction of Teradata Vantage with Amazon SageMaker gives organizations ubiquitous use of advanced analytics to unlock the full power of their data

SAN DIEGO--(BUSINESS WIRE)-- Teradata (NYSE: TDC) today announced the integration and general availability of the Teradata Vantage multi-cloud data and analytics platform with Amazon SageMaker, the industry’s most complete end-to-end machine learning (ML) service. By uniting the scalability and openness of Vantage with the intuitive ML model capabilities of Amazon SageMaker, Teradata and Amazon Web Services (AWS) are solving the scalability dilemma for enterprises worldwide.

This move supports Teradata’s analytics framework, Analytics 123 which gives organizations that are challenged with production-level artificial intelligence (AI)/ML initiatives a step-by-step solution for deploying analytical models at scale. Together, the Analytics 123 framework and the integration of Vantage with Amazon SageMaker drive faster time-to-value and greater return on investment (ROI).

Despite massive investments in AI/ML and other advanced analytics, many organizations have yet to deploy these solutions widely and are struggling to see the true value of their promise. This is because data preparation for AI/ML is time consuming and costly, with data wrangling still accounting for up to 80% of the cost and effort of analytic projects. In the Gartner® report "Top Trends in Data and Analytics for 2021" they say "Many organizations struggle to scale their AI prototypes and pilots to full production and wider usage, and often underestimate the challenge of deploying and integrating AI with other systems. According to the 2020 Gartner AI in Organizations Survey, only 53% of prototypes are eventually deployed." And certainly not at scale and across organizational silos.

Teradata Vantage delivers enterprise-scale performance to ensure that even the largest customers can execute complex analytics on massive datasets while using their favorite data science tools and languages - such as Amazon SageMaker. Amazon SageMaker enables developers to create, train, and deploy AI/ML models on the cloud, embedded systems, and edge devices. Tens of thousands of active customers use Amazon SageMaker to train models with billions of parameters to make hundreds of billions of predictions every month.

Vantage and Amazon SageMaker work seamlessly, giving customers the ability to continuously score complex ML models at scale. AI/ML projects can move into wide-scale production in weeks instead of months and organizations can score data, like customer or parts information, in minutes instead of hours and days. The result is that customers can now rapidly accelerate their AI/ML projects to deliver data-driven insights and realize real-world business outcomes.

“Our enterprise customers are investing in the possibility of AI/ML to prevent fraud, reduce churn, optimize supply chains, and prevent catastrophic infrastructure failures,” said Hillary Ashton, Chief Product Officer at Teradata. “Many technology providers claim to offer AI/ML solutions to meet these critical needs – but they cannot implement them at scale. Teradata is, and always has been, an innovator in enterprise analytics and data warehousing, solving the toughest data challenges in the most complex and demanding environments. Now we are combining Vantage’s flexibility, scalability, and deep advanced analytics capabilities with Amazon SageMaker’s versatility in creating ML models to give enterprises the speed, simplicity, and scale they need to derive genuine business value from their AI investments.”

"Amazon SageMaker is one of the fastest growing services in AWS history and we are continuing to invest in expanding the service capabilities as customers further scale their machine learning models for training and inference on Amazon SageMaker," said Omer Zaki, General Manager, SageMaker Foundations, AI Platforms at AWS.

“Being a data-driven organization allows us to make informed decisions to create a better guest and team member experience,” said Pankaj Patra, Senior Vice President and Chief Information Officer at Brinker International. “As we looked for more flexible and cost-effective ways to manage and access our data, we evaluated quite a few cloud-native providers. After careful consideration, we decided the best course of action would be to migrate to Teradata Vantage on AWS and take advantage of its offerings to support our advanced analytic goals.”

To learn more about Teradata Vantage with Amazon SageMaker:

  • Try our Amazon SageMaker Hands-on Lab by creating an account on Teradata University and selecting the “Teradata Vantage and Amazon SageMaker” course
  • Visit Teradata at booth #111 at the Amazon re:MARS event June 21-24, 2022 in Las Vegas
  • Request a demonstration by emailing aws@teradata.com

Availability

Teradata Vantage with Amazon SageMaker is available today, worldwide.

Gartner, Top Trends in Data and Analytics for 2021, Rita Sallam, Donald Feinberg, Pieter den Hamer, Shubhangi Vashisth, Farhan Choudhary, Jim Hare, Lydia Clougherty Jones, Julian Sun, Yefim Natis, Carlie Idoine, Joseph Antelmi, Mark Beyer, Ehtisham Zaidi, Henry Cook, Jacob Orup Lund, Erick Brethenoux, Svetlana Sicular, Sumit Agarwal, Melissa Davis, Alan D. Duncan, Afraz Jaffri, Ankush Jain, Soyeb Barot, Saul Judah, Anthony Mullen, James Richardson, Kurt Schlegel, Austin Kronz, Ted Friedman, W. Roy Schulte, Paul DeBeasi, Robert Thanaraj, February 2021

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

About Vantage

Teradata Vantage is the connected multi-cloud data platform for enterprise analytics. It enables ecosystem simplification by unifying analytics, data lakes and data warehouses. With Vantage, enterprise-scale companies can eliminate silos and cost-effectively query all their data, all the time, regardless of where the data resides — in the cloud, on multiple clouds, on-premises, or any combination thereof — to get a complete view of their business.

About Teradata

Teradata is the connected multi-cloud data platform for enterprise analytics company. Our enterprise analytics solve business challenges from start to scale. Only Teradata gives you the flexibility to handle the massive and mixed data workloads of the future, today. Learn more at Teradata.com.

The Teradata logo is a trademark, and Teradata is a registered trademark of Teradata Corporation and/or its affiliates in the U.S. and worldwide.

January Machold

858-485-3049 office

January.Machold@Teradata.com

Source: Teradata

FAQ

What is the purpose of the Teradata and Amazon SageMaker integration?

The integration aims to enhance advanced analytics capabilities and improve scalability for AI/ML deployment.

When was the Teradata Vantage with Amazon SageMaker announced?

The integration was announced on June 9, 2022.

How does Teradata Vantage benefit enterprises?

It offers enterprise-scale performance for executing complex analytics on large datasets and accelerates the deployment of AI/ML projects.

What challenges in AI/ML deployment does this integration address?

It addresses the scalability dilemma and aims to streamline data preparation costs, which account for up to 80% of analytic project efforts.

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