Successful AI/ML Projects with End-to-End Data Management - Databricks

Successful AI/ML Projects with End-to-End Data Management

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.



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About Razi Sharir

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.

About Sumeet Agrawal

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.