Every healthcare and life sciences organization wants to accelerate innovation by incorporating AI into their business. In fact, machine learning has become essential for use cases such as patient risk scoring, real-world evidence analytics, biomedical image analysis, genomics and more. However, most companies struggle with preparing large clinical and biomedical datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production.
In this workshop, we’ll cover best practices for using powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™ for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use popular ML frameworks to train models for various healthcare and life sciences use cases. We’ll also provide a deep dive on Databricks.
Join this workshop to learn how a unified approach to analytics can accelerate ML projects in the Healthcare and Life Sciences industry.
Agenda at a Glance
8:30-9:00 Registration, Breakfast & Networking
9:00-9:30 Opening Remarks - Unifying Data Science and Data Engineering
9:30-10:00 Guest Speaker: Adam Petranovich, Chief Data Scientist at Prognos
10:00-10:30 Networking with Peers
10:30-11:15 Data Engineering Demo: Exploratory Analysis on Real-World Evidence Data
11:15-12:00 Data Science Demo: Using ML to Predict Cost of Next Patient Visit
Space is limited for this event. Sign up today to reserve your spot!