Teradata on Wednesday unveiled a new integration between its VantageCloud platform and Microsoft Azure Machine Learning designed to help joint customers more successfully build and deploy augmented intelligence and machine learning models.
Based in San Diego, Teradata is a longtime data management and analytics vendor whose Vantage platform was first launched in 2019.
Built to be cloud-first at the time of its initial launch but not fully cloud native, the platform was renamed VantageCloud Enterprise in August 2022 when Teradata launched a fully cloud-native version of its suite called VantageCloud Lake.
In addition to VantageCloud, Teradata offers a BI platform, ClearScape Analytics, that enables users to work with data in-database rather than require them to extract, transform and load data before running queries and analysis.
Azure Machine Learning (Azure ML), meanwhile, is Microsoft's cloud service for building, training and managing machine learning models.
Teradata and Microsoft launched a global partnership in February 2022. At the time, the vendor and tech giant were already closely aligned with Teradata's Vantage platform integrated with Microsoft's Power BI, Azure Synapse Analytics and dozens of other data services.
Now, Teradata and Microsoft have integrated VantageCloud with Azure ML in a development that aims to make it easier for organizations to move AI and ML models from the idea stage through production.
According to studies, the vast majority of AI and ML models never make it into production. Some of the reasons they fail at such a high rate include the difficulty of finding relevant and accurate data often kept within different departments and organizations lacking enough employees with data science skills.
But with the combination of tools the Teradata-Microsoft integration provides that are designed to break down barriers that keep data isolated and simplify the process of producing models, the integration should help organizations more successfully put models into production, said Mike Leone, an analyst at Enterprise Strategy Group, a division of TechTarget.
More organizations than ever are trying to derive value from AI and ML, and they're coming up with an ever-increasing number of applications for data science models, he noted.
Mike Leone Analyst, Enterprise Strategy Group
With VantageCloud and Azure ML now integrated, joint Teradata and Microsoft customers have the tools to execute their model development plans.
"In order to achieve the lofty scale goals of putting AI to work across an organization, there needs to be a new level of integration, automation, reliability and optimization to ensure that stakeholders such as data scientists and developers are set up for success," Leone said. "This enables Teradata customers to achieve just that."
While the new integration stands to benefit joint Teradata and Microsoft users, Teradata also has a similar integration with Amazon SageMaker designed to help joint Teradata and AWS customers better develop and deploy AI and ML models.
A similar integration with Google Cloud's modeling tools is also in the works, said Hillary Ashton, Teradata's chief product officer, in August 2022.
As a result, perhaps the most significant aspect of the integration between VantageCloud and Azure ML is that it enables Teradata customers to avoid vendor lock-in, according to Donald Farmer, founder and principal of TreeHive Strategy.
"The Teradata announcement is important in so far as it [adds to] the compatibility of Teradata Vantage with major could platforms," he said. "This addresses customer concerns about lock-in to a cloud platform."
Beyond the freedom it gives Teradata customers to pair VantageCloud with the cloud platform of their choice, the integration adds some functionality, Farmer continued. In particular, it adds workflow management and security features.
"It does round out the Teradata portfolio nicely," he said.
As Teradata plots its roadmap, AI and ML will feature prominently, the vendor said.
But beyond just VantageCloud, the vendor plans to enhance the AI and ML capabilities of ClearScape Analytics.
Meanwhile, Teradata's technology is strong relative to peers such as Databricks, Snowflake, MongoDB and SingleStore, Leone and Farmer said.
Therefore, pricing is an area in which Farmer said Teradata should focus its attention.
The vendor offers numerous pricing options. All are consumption-based, and range from $9,000 per month to $31,000 per month. But because they begin at what amounts to more than $100,000 annually for even the most inexpensive option, Teradata appeals to mostly large enterprises, Farmer noted.
"They have a very complete offering already, but the audience is still limited to relatively high-end enterprise use cases," he said. "So, I think they should look to embrace the open source community more effectively, both through pricing and better APIs for the developer community."
Leone, for his part, said Teradata is wise to continue prioritizing AI and ML.
He noted that the vendor has a comprehensive platform with tools addressing data warehousing, analytics and strategic consulting. Therefore, even though Teradata has already made AI and ML a focus -- including partnerships with AI specialists -- it should hone in even further on those capabilities.
"I see significant potential if they were to double down on the time, energy and investments being made in better enabling customers to scale the use of AI and machine learning," Leone said.
Eric Avidon is a senior news writer for TechTarget Editorial and is a journalist with more than 25 years of experience. He covers analytics and data management.