Enterprises can now execute analytics at scale with Teradata Vantage
Teradata has added bring your own model (BYOM) functionality to Vantage, in a move that gives data-driven enterprises a step by step solution for deploying analytical models at scale.
According to the company, businesses will be able to quickly realise a greater return on investment (ROI) in developing analytical models through increased model operationalisation, expanded analytic use cases, and a streamlined approach to data-driven decision-making.
Teradata’s strategic analytics framework, Analytics 1-2-3, establishes a roadmap for businesses to create robust, efficient, and 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 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 operationalised and scaled to the level required to score the various models rapidly, securely and consistently, all within Vantage, Teradata states.
By leveraging this new functionality, Analytics 1-2-3 provides Teradata customers with a way to create and operationalise 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, the company states.
This is realised 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 operationalisation of insights without needing to sample data or create data silos outside of Vantage.
Teradata has a partnership community with advanced analytics and AI/ML vendors. The Analytics 1-2-3 framework incorporates existing and new partners in Teradata’s analytics and AI/ML portfolio.
Teradata chief product officer Hillary Ashton says, “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.
"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 organisations that wish to quickly realise value from their AI/ML investments.”
Analytics 1-2-3 is made up of the following.
Analytics 1 - Data Preparation: This is when any type of data and at whatever volume is prepared. The core features are then extracted which are in turn used for analytic modelling. These features, once created, are curated within an Enterprise Feature Store (EFS) so that they can be repeatedly used.
Analytics 2 - Model Training: This is when analytical models (e.g., machine learning, statistical) are created from the features delivered in the first step.
Model functionality that is natively available in Vantage, as well as the BYOM functionality, ensures that a wide range of models, often used in combination, are made available for operationalisation.
Analytics 3 - Deployment: With Vantage’s AnalyticsOps service, users can manage end-to-end analytic model creation at scale. Vantage will monitor model performance and automatically trigger rescoring or model updates, all while maintaining model, features, code, and data lineage.