Preparing and Designing the Data Lakehouse for Artificial Intelligence
Just as software has transformed the business landscape, so will AI. Most of the time spent in developing AI is focused on data, and if you get the data right, most of the problems are solved at the beginning of designing the pipeline.
In this lecture, Vini Jaiswal will cover the modern data paradigm called the data lakehouse, which provides all of the capabilities needed to train and deploy machine learning models, and an overview of the end-to-end Databricks machine learning, which is built on an open lakehouse foundation. Vini will also share her experiences working with data practitioners in different industries who are using the lakehouse to build AI applications.
Vini Jaiswal is a developer advocate at Databricks and brings 9+ years of data and cloud experience working with unicorns, digital natives, and Fortune 500 companies. She helps data practitioners to be successful in building on Apache Spark, Delta Lake, Databricks, MLflow, and other open source technologies. Previously, Vini was Citi’s VP engineering lead for data science, where she drove engineering efforts including the one where she led the deployment of highly scalable data science and ML architecture on the global cloud. She also interned as a data analyst at Southwest Airlines and holds an MS in information technology and management from the University of Texas at Dallas. Currently, she is co-authoring the book Delta Lake: The Definitive Guide by O’Reilly.