Teradata accelerates large-scale deployment of AI and machine learning projects in Teradata Vantage with Amazon Sagemaker

Teradata (NYSE: TDC) has announced the integration and global availability of the multi-cloud data and analytics platform Teradata Vantage with Amazon SageMaker, the industry’s most comprehensive end-to-end machine learning (ML) service. By combining the scalability and openness of Vantage with the intuitive ML model building capabilities of Amazon SageMaker, Teradata and Amazon Web Services (AWS), large-scale industrialization dilemmas for global enterprises are being solved.

This method is part of Teradata’s analytical framework, Analytics 123. It offers companies experiencing artificial intelligence (AI) / ML initiatives at the production level, a step-by-step solution to establishing large-scale analysis of models. Collectively, the integration of Vantage with Analytics 123 Framework and Amazon Sagemaker increases the time value and return on investment (ROI).

Despite massive investments in AI / ML and other advanced analytics processes, many companies have not yet been able to scale these solutions and struggle to see the true value of their promises. This is because data preparation for AI / ML is very time consuming and costly, and is responsible for data processing up to 80% of the cost and effort generated while running projects.

“Many companies strive to scale their AI prototypes and pilots to full production and greater use and often underestimate the challenge of deploying and integrating AI into other systems,” according to the report “Top Trends in Data and Analysis for 2021”. . In addition, according to its 2020 Enterprise AI Survey, Gartner reveals that only 53% of prototypes are finally deployed. Nevertheless, it is certain that these prototypes are not placed on a large scale or in all organizational silos.

To ensure that Teradata Vantage provides enterprise-scale performance, even the largest customers can perform complex analysis on large datasets using their favorite data science tools and languages. Amazon SageMaker enables developers to create, train and deploy AI / ML models in the cloud, as well as on embedded systems and devices. Thousands of active customers use Amazon SageMaker to train models with billions of parameters to predict billions each month.

Vantage and Amazon SageMaker work seamlessly, allowing customers to scale complex ML models on a scale. Thus, AI / ML projects can be produced on a weekly scale instead of a month, and companies can evaluate data – such as customer or partial information – in minutes or hours instead of days. Customers are now able to accelerate their AI / ML projects to provide data-driven insights and drive real business results.

“Our business clients choose to invest in AI / ML and its multiple possibilities to thwart any attempt at fraud, reduce customer losses, optimize the supply chain and prevent serious infrastructural failures,” said Hillary Ashton, Chief Product Officer at Teradata. Claims to offer AI / ML solutions to deal with – but they can’t scale them. Teradata is always innovative in enterprise data analytics and data storage, and able to solve the most difficult data challenges in the most complex and demanding environments. Combines the flexibility, scalability, and deeply advanced analytics capabilities of Amazon SageMaker with versatility to give businesses the speed and simplicity they need.

“The Amazon Sagemaker service has arguably seen the fastest growth in AWS history. That’s why we’re continuing to invest in expanding its capabilities as our customers are increasingly scaling their ML models at Amazon SageMaker for training and forecasting, ”said Om Jockey, General Manager, Sagemaker Foundation, AWS’s AI Platform.

“Being a data-driven company allows us to make informed decisions to create the best possible experience for our guests and our team,” commented Pankaj Patra, Senior Vice President and Chief Information Officer at Brinker International. “Since we wanted to manage and access our data more flexibly and economically, we evaluated a number of cloud-native service providers. After careful consideration, we’ve decided that the best way to move forward is to transfer our data from AWS to Teradata Vantage and take full advantage of its offers to support us in our advanced analysis goals. A

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