Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data management: finding & discovering, ingesting & integrating, serverless processing at scale, and data governance. Stop by this session for an overview and demo (we’ll showcase a fraud detection use case) on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data management.
Razi Sharir is the VP of Product Management for Informatica Big Data products. Previously, he was the VP of Products at Robin, a Kubernetes-based Big Data platform and the CEO for Xeround, a multi-cloud DBaaS. Earlier, Razi was at BMC Software’s Innovation Lab, and at Microsoft’s OS/Server and Desktop businesses.
Sumeet Agrawal is a Senior Director of Product Management for Informatica's Big Data products. He is responsible to define product direction, roadmap, and long-term strategy for Informatica’s big data offerings. Sumeet’s expertise includes Hadoop ecosystem, cloud (AWS, Azure), Apache Spark, serverless, Java, among other technologies.