In this tutorial we will present Koalas, a new open source project that we announced at the Spark + AI Summit in April. Koalas is an open-source Python package that implements the pandas API on top of Apache Spark, to make the pandas API scalable to big data. Using Koalas, data scientists can make the transition from a single machine to a distributed environment without needing to learn a new framework.
We will demonstrate Koalas’ new functionalities since its initial release, discuss its roadmaps, and how we think Koalas could become the standard API for large scale data science.
What you will learn:
Tim Hunter is a software engineer at Databricks and contributes to the Apache Spark MLlib project, as well as the GraphFrames, TensorFrames and Deep Learning Pipelines libraries. He has been building distributed Machine Learning systems with Spark since version 0.2, before Spark was an Apache Software Foundation project.
Brooke Wenig is the Machine Learning Practice Lead at Databricks. She advises and implements machine learning pipelines for customers, as well as educates them on how to use Spark for Machine Learning and Deep Learning. She received an MS in Computer Science from UCLA with a focus on distributed machine learning. She speaks Mandarin Chinese fluently and enjoys cycling.
Niall Turbitt is a Data Scientist on the Machine Learning Practice team at Databricks. Working with Databricks customers, he builds and deploys machine learning solutions, as well as delivers training classes focused on machine learning with Spark. He received his MS in Statistics from University College Dublin and has previous experience building scalable data science solutions across a range of domains, from e-commerce to supply chain and logistics.