AI on Greenplum Using Apache MADlib and MADlib Flow
Frank McQuillan is Director of Product Management at Pivotal, focusing on analytics and machine learning for large data sets. Prior to Pivotal, Frank has worked on projects in the areas of robotics, drones, flight simulation, and advertising technology. He holds a Masters degree from the University of Toronto and a Bachelor's degree from the University of Waterloo, both in Mechanical Engineering.
My name is Sridhar Paladugu, I am working at Pivotal Software from past 4+ years. At Pivotal I work with customers on all things around data including Spring, java, PCF, Gemfire and most importantly Greenplum. My current focus is on scaling Machine learning and AI on Greenplum and Postrges on Containers. I am the project lead and developer for and upcoming tool MADlib Flow. Before Pivotal I spent several years in building trading applications using spring, java, Hadoop, etc at Bank of America Merrill Lynch. I am passionate about building software and helping customers. When i am not working i enjoy spending time with family and an occasional hiking trips.
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Advanced analytics and machine learning are rapidly growing in importance in enterprise computing. Key enterprise data typically resides in relational form, and it is inefficient to copy data between systems to perform analytical operations.
In addition to leveraging the rich set of Postgres analytics like window functions, Greenplum offers machine learning, graph analytics, statistics, and data transformations via the mature Apache MADlib open source project. These capabilities are all Postgres-compatible but designed for massively parallel use cases.
When it comes to deploying to production, modern enterprise AI deployments are ecosystems of machine learning solutions that tightly integrate a feedback loop triggering automated updates to the underlying algorithms, and thus creating closed loop machine learning systems. MADlib Flow has been designed for containerized deployment of AI pipelines to Kubernetes, where Cloud Foundry with Postgres play a key role for low latency prediction.
In this session, we will give an overview of Apache MADlib on Greenplum and MADlib Flow. Topics will include scalability results, roadmap, and an example of a real-time financial transaction fraud prevention system that continuously learns new threat signatures.
- 2019 March 19 10:00 EDT
- 50 min
- Postgres Conference
- Greenplum Summit