Advanced Computing in the Age of AI | Friday, June 9, 2023

Teradata and Google Cloud Announce New Machine Learning Integration 

A new integration for machine learning is available from Teradata and Google Cloud. Google Cloud’s Vertex AI platform is now generally available with Teradata VantageCloud and ClearScape analytics.

“With Teradata VantageCloud and ClearScape Analytics plus Vertex AI, organizations can move seamlessly from being AI experimenters to AI achievers,” Teradata said in a release.

Vertex AI is Google Cloud’s end-to-end machine learning platform introduced in 2021. Teradata says Vertex AI helps users take advantage of various cutting-edge algorithms to build high-quality AI models in less time and with minimal expertise. Teradata’s VantageCloud is the cloud version of the company’s long-standing data warehouse and comes in two editions: Enterprise, optimized for high-end production analytics workloads, and Lake, optimized for data science and exploratory analytics.

ClearScape Analytics is a suite of in-database analytics and machine learning tools that can run on any Teradata environment and was designed to be used in conjunction with a data science notebook. ClearScape features MLOps capabilities to help data scientists automate the ML lifecycle, including capturing, training, deploying, and monitoring ML models in production.

The combination of these elements enables faster and more sophisticated AI models that can be scaled across an organization, according to Teradata. Customers using VantageCloud on Google Cloud can integrate disparate datasets from multiple environments, data lakes, and object stores to help streamline data preparation, while ClearScape Analytics can transform the data into reusable analytic datasets. These datasets can then be used to build and train ML models with Vertex AI. Vertex AI models can be operationalized at scale in VantageCloud to give customers direct, transparent, and real-time access to all their models, Teradata says.

“Our customers are investing in the power of AI to fuel their digital transformations and achieve tangible business outcomes that have a real-world impact on their businesses,” said Hillary Ashton, chief product officer at Teradata, in a release. “Our openness and scalability facilitate the operationalization of Vertex AI’s models across an organization and its mission-critical use cases – such as customer churn, fraud detection, predictive maintenance, and supply chain optimization. Customers are able to make bold business decisions, driven by data, that keep them ahead of the competition.”

“Vertex AI enables data scientists to build, deploy and scale machine learning models faster, with fully managed tools and services for use cases across industries. This capability, when combined with the vast and reliable analytics data sets prepared by Teradata, gives customers the ability to scale their AI/ML initiatives quickly and with confidence, speeding time to value,” said June Yang, VP, cloud AI and industry solutions at Google Cloud, in a release.

Teradata seems to be focused on bolstering its customers’ machine learning capabilities. The company also recently announced the general availability and integration of VantageCloud and ClearScape with the Microsoft Azure Machine Learning platform.

Shares in Teradata rose 6% on Monday after Wall Street analyst Howard Ma of Guggenheim Partners raised his rating on the company. Ma suggests that Teradata may be experiencing a positive turning point when it comes to customer retention. Though reports have said Teradata has been losing customers to other cloud competitors, Ma claims that recent conversations with Teradata partners may indicate an increased demand for staying with the company.

“What many thought was impossible may be starting to happen,” Ma told investment news outlet Seeking Alpha. “The complex workloads tied into core business logic are likely there to stay on Teradata in the near-and-mid-term, so the rate of decay in [the company’s] installed base will likely be slower going forward.”

This article originally appeared on sister site Datanami.