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Every healthcare and life sciences organization wants to accelerate innovation by incorporating advanced analytics 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 organizations struggle with preparing large clinical and biomedical datasets for analytics, managing the proliferation of ML frameworks, and moving models from 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™ and Delta Lake for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use popular ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models for various healthcare and life sciences use cases. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production.

Join this workshop to learn how a unified approach to data analytics can accelerate machine learning projects in the Healthcare and Life Sciences industry.

8:30-9:00 Registration, Breakfast & Networking 

9:00-9:30 Opening Remarks - Unifying Data Science and Engineering 

9:30-10:00 Healthcare and Life Sciences Customer Stories and Use Cases 

10:00-10:30 Networking with Peers

10:30-11:15 Data Engineering Demo: Exploratory Analysis of Real-World Evidence Data

11:15-12:00 Data Science Demo: Using ML to Predict Polygenic Risk with Genetic Data

12:00-12:15 Q&A 

Space is limited for this event. Sign up today to reserve your spot!