Informatica LLC is looking to increase its relevance in generative artificial intelligence application development with the release today of several “blueprints” that outline the best way to create AI applications on different cloud infrastructure platforms.
The Generative AI Blueprints are aimed at making it easier for companies to build enterprise-grade generative AI-powered apps quickly on Amazon Web Services, Google Cloud, Microsoft Azure, Oracle Cloud, Snowflake and Databricks. They contain standard reference architectures and a number of what the company calls “ecosystem-specific recipes” to help customers get started. In addition, it provides valuable building blocks in the shape of its GenAI Model-as-a-Service and vector database connectors.
According to Informatica, what it wants to do is help enterprises make the most out of the “AI-ready data.” Of course, the blueprints conveniently include guidelines and configurations for using its flagship Intelligent Data Management Cloud platform as a key component of generative AI applications.
Informatica’s Intelligent Data Management Cloud is used by companies to transfer data between disparate systems. For example, a customer might use it to bring data from its sales logs into an analytics platform to try to get insights into consumer buying habits. Informatica’s platform does all of the heavy lifting in this process, syncing information between the different systems, reformatting the data and so on.
The company believes its platform can perform a similar role for generative AI applications that must be fed on massive amounts of corporate data. For instance, generative AI chatbots need to be able to access an organization’s internal systems to field customer inquiries, assist users with their IT problems and so on.
Informatica cites the example of the professional services firms Deloitte Touche Ltd. and Capgemini Inc., which have both built generative AI platforms using its blueprints. It says the blueprints help solve a number of headaches for developers and engineers, including issues relating to metadata, data discovery, data engineering, access control and policy enforcement.
The blueprints for AWS, Azure, Google Cloud and Oracle Cloud can be found within Informatica’s Architecture Center, and are free for everyone. Additional blueprints for Databricks and Snowflake will be published early next year.
The Intelligent Data Management Cloud will act as the foundation of customer’s generative AI apps, ensuring that they’re enriched with every bit of data they require to facilitate the tasks they’re designed for. The blueprints also enable companies to use a “no-code” approach to build a robust “scaffolding” for delivering and managing their generative AI applications. And with the included recipes, which provide instructions for building bare-bones apps that can then be fine-tuned and customized, enterprises can get started much faster, the company promised.
Rik Tamm-Daniels, vice president of strategic ecosystems at Informatica, said organizations need to bring together a myriad of components to get started in generative AI app development. These include the underlying large language models, vector data management tools, prompt handling systems and so on. “These blueprints provide a prescriptive path for customers to unlock the potential of generative AI for their enterprise use cases,” he said.
Deloitte’s principal and applied AI leader Jim Rowan said Informatica’s blueprints can be useful to enterprises looking for a way to implement generative AI while meeting the highest standards in terms of data quality and compliance. “It requires a robust foundation of data management,” he said. “Informatica’s blueprints elevate the practice of developing and delivering responsible generative AI at scale.”
Peter Ku, vice president and chief industry strategist of banking, capital markets and financial services at Informatica, sat down with John Furrier, host of SiliconANGLE Media’s livestreaming studio theCUBE, earlier this year to discus how the company is aiming to reinvent data management for AI workloads.