Teradata, the connected multi-cloud data platform for enterprise analytics, announced that Vantage can now operationalise externally created predictive models, a practise known as model sharing or BYOM. This move strengthens Teradata's strategic analytics framework, which provides data-driven enterprises with a step-by-step approach to deploying analytical models at scale. Through increased model operationalisation, expanded analytic use cases, and a streamlined approach to data-driven decision-making, businesses will now be able to quickly realise a higher return on investment (ROI) in developing analytical models.
Business leaders recognise that artificial intelligence and machine learning are the basis of competitive advantage in their industry, leading to an explosion in AI/ML technology investments. Despite these investments, many businesses are struggling to see returns from
AI/ML projects due to inefficient data processes. To address this challenge, Teradata has created a strategic analytics framework – Analytics 1-2-3 – to establish a straightforward roadmap for businesses to create robust, efficient, and easily deployed processes that ensure AI/ML projects live up to their promise and deliver business value.
BYOM complements this framework by making a much broader set of models and analytic algorithms available for large-scale deployment. This means that models that were previously developed on small systems with limited data sets can now be operationalised and scaled to the level required to score the various models quickly, securely, and consistently, all within Vantage. Analytics 1-2-3, by leveraging this new functionality, enables Teradata customers to easily 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. 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 robust partnership community with leading advanced analytics and AI/ML vendors. The Analytics 1-2-3 framework naturally incorporates existing and new partners in Teradata’s analytics and AI/ML portfolio, providing customers, and the data science community, with the industry’s most optimal analytics ecosystem. Teradata provides customers support for their analytic tools of choice – now, in an industry-best, optimised configuration. The result is an analytics ecosystem that provides business users with answers and insights in minutes rather than hours or days, enables more robust models for deeper insights, and delivers faster model refresh, updates, and replacements.
“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,” said Hillary Ashton, Chief Product Officer at Teradata. “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: A Strategic Framework Leveraging New BYOM Capabilities
Analytics 1-2-3 makes it simple to create and run any number of models on any data volume in near real time. It decouples the various elements of the analytics process and ensures that each is given the appropriate weight. When combined with Vantage's BYOM functionality, this framework enables enterprises to quickly and efficiently test, scale, and deploy analytical models.
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 modeling. These features, once created, are curated within an Enterprise Feature Store (EFS) so that they can be repeatably 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.
Teradata Vantage is a multi-cloud data platform for enterprise analytics that is connected. It simplifies the ecosystem by bringing together analytics, data lakes, and data warehouses. To get a complete view of their business, enterprise-scale companies can use Vantage to eliminate silos and cost-effectively query all of their data, all of the time, regardless of where the data resides — in the cloud, on multiple clouds, on-premises, or any combination thereof.