SAN FRANCISCO–(BUSINESS WIRE)–Databricks, the leader in unified data analytics, today announced keynote speakers alongside expanded technical content and training at Spark AI Summit which is taking place June 22 – 25 in San Francisco. 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 engineers, scientists, developers, analysts and leaders from around the world. "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. Spark AI Summit has become the destination for data teams to collaborate on solutions to solve the world's toughest problems," said Ali Ghodsi, cofounder and CEO at Databricks.

Spark AI Summit is the premier global event for the data and machine learning community to discuss the latest advances in open-source technologies such as Apache Spark, Delta Lake, MLflow, Koalas and TensorFlow as well as best practices for deploying AI in the real world. In addition to over 100 exciting breakout sessions, this year's Spark AI Summit in Amsterdam 15-17 October will feature keynotes from some of the leading thinkers and innovators in AI. We're pleased to announce Katie Bouman's keynote address at this year's Spark AI Summit Europe. Katie was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics, and she received her Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory in EECS. Currently, she specializes in using emerging computational methods to push the boundaries of imaging.

Don't look now but Apache Spark is about to turn 10 years old. The open source project began quietly at UC Berkeley in 2009 before emerging as an open source project in 2010. For the past five years, Spark has been on an absolute tear, becoming one of the most widely used technologies in big data and AI. Let's take a look at Spark's remarkable run up to this point, and see where it might be headed next. Apache Spark is best known as the in-memory replacement for MapReduce, the disk-based computational engine at the heart of early Hadoop clusters.

In this talk, Richard Garris, Principal Architect at Databricks will explain how various ML algorithms are parallelized in Apache Spark. Andrew Ng calls the algorithms the "rocket ship" and the data "the fuel that you feed machine learning" to build deep learning applications. We will start with an understanding of machine learning pipelines built using single machine algorithms including Pandas, scikit-learn, and R. Then we will discuss how Apache Spark MLlib can be used to parallelize your machine learning pipeline with Linear Regression and Random Forest. Lastly, we will discuss ways to parallelize single machine algorithms in Spark by broadcasting the data and then performing distributed feature selection, model creation or hyperparameter tuning. Bio: Richard Garris is a Principal Solutions Architect at Databricks focused on helping clients with their Advanced Analytics initiatives using Apache Spark and MLlib.

Join 4,000 of the top developers, data scientists, and business executives who will be tuning into the sessions and training at this year's Spark AI Summit. Use code KDnuggets for 30% off conference pass when you register by May 18, 2018. Data and AI need to be unified: the best AI applications require massive amounts of constantly updated training data to build state-of-the-art models. Apache Spark has been the only unified analytics engine that combines large-scale data processing with the execution of state-of-the-art machine learning and AI algorithms. In addition to the over 180 session talks at this year's Summit, we are thrilled to highlight some of our renowned keynote speakers.