Accelerating innovation using tiered AI Architecture 

Prior research demonstrated with the Minerva platform that there is a need for easy-to-do machine learning (ML) frameworks. Additionally, SaaS applications are closely related when they form an application suite, which brings forth the need for an ML framework that can facilitate the “lift and shift” of ML model code in similar needs in multiple enterprise applications in a suite. This study recommends a portable ML microservice framework, Minerva (also known as contain second generation), a microservices-based container framework for Applied Machine Learning, as an efficient way to modularize and deploy intelligent microservices in both traditional “legacy” SaaS application suites and cloud environments, especially in the enterprise domain. This study identifies that there is an impetus to innovate quickly for machine learning features in enterprise SaaS applications. The study highlights the real-world implementation of Minerva for “lift and shift,” doubling innovation speed with the same resources. It evaluates ML model code reusability across applications, resulting in 1.15 to 2X faster adoption compared to previous methods in a marketing application suite. Secondly, while a layered design accelerated innovation through porting existing models to related business suites, generative AI methods, while promising, have not yet yielded significant gains with smaller models already optimized with no code solutions.

Venkata Duvvuri is currently a Senior Scientist Tech Lead at Nvidia and a Founder of SiriusMindShare labs. His previous responsibility was an Director of data science at Oracle corporation. He is results oriented data science, business, web & marketing analytics leader with over 12 years’ experience in Data science, Business Analytics, Digital Media Analytics and Web Marketing optimization with leading internationally recognized corporations, additional 10 years software engineering experience and a top business school MBA. He has experience in management of onshore and offshore teams as well as leadership experience ranging from Director to Manager at Fortune 100 companies including marketing agencies. He is a cross functional leader and subject matter expert in machine learning. He has improved ad performance to the tune of $10M at Unity Technologies & $7M at Oracle leveraging deep learning, spark MLIB and Tensorflow operating on big data. He has driven improvements in business & online marketing outcomes to the tunes of several million dollars at eBay (Triad Retail Media), Conversant (Alliance Data), AT&T (Cricket Wireless) and crunched several terabytes of data at Apple Corporation. He holds a Masters in Computer science from University of Massachusetts – Amherst and a MBA from University of California- Davis. Additionally, he is a doctoral candidate at Purdue university. Finally, he serves on the advisory alumni board at Graduate school of Management at UC Davis.