Unified data analytics platform Databricks has announced keynote speakers alongside expanded technical content and training at Spark + AI Summit which is taking place June 22 - 25 in San Francisco.
The keynote lineup spans data and machine learning innovators to data visionaries, including Nate Silver of FiveThirtyEight.com, Jennifer Chayes of UC Berkeley, and Adam Paszke of PyTorch. To support continuous innovation and expansion of the conference content, Spark + AI Summit welcomes Ben Lorica as the Program Chair.
Spark + AI Summit is the largest data and machine learning conference bringing together professionals and leaders from around the world, including:
“Over four days we’ll gather the greatest minds in our industry to shape the future of big data, analytics, and AI and share knowledge through training, over 180 talks and networking events,” said Ali Ghodsi, cofounder and CEO at Databricks.
More than 7,000 attendees at Spark + AI Summit will attend four days of training sessions, presentations, and networking events. This year’s Summit has tripled the number of technical training sessions covering key technologies and topics, including Apache Spark, Delta Lake, MLflow, TensorFlow, deep learning, applying software engineering principles to data engineering and machine learning.
The Summit kicks off with pre-conference training workshops, including both instruction and hands-on classes, which has now expanded to two days of full-day and half-day courses.
Databricks executives and original creators of popular open source projects including Apache Spark, Delta Lake, MLflow, and Koalas will also hit the keynote stage:
Ben Lorica is the former Chief Data Scientist at O’Reilly Media, and the former Program Chair of: the Strata Data Conference, the O’Reilly Artificial Intelligence Conference, and TensorFlow World. The Summit will leverage Lorica’s industry expertise to continuously innovate and expand the Summit training, sessions and keynotes.
Find out more information about Spark + AI Summit 2020 and register to attend.