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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Razi Sharir
About Razi Sharir

Razi Sharir joined Tableau as the VP of Product Management in 2020. Prior to Tableau, he was the VP of PM at Informatica, leading Data Management on-pre mand cloud. Prior to Informatica, Razi was the VP of Products and Marketing at Robin, developing world first hyper converged Kubernetes decoupled storage and compute platform. Prior experiences include Xeround DBaaS (at the time the most popular service second to AWS), developing and running BMC Innovation Lab and early on at Microsoft. Razi holds a Masters in Business (MBA/marketing) and a Masters in Philosophy.

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.