[Ai4 Webinar] Modernizing ML & AI Operations to Advance Healthcare
This webinar is a collaboration between Ai4 and Qubole. Qubole is a cloud-native cloud platform built specifically for self-service AI and ML applications. Key Takeaways: - Challenges of Healthcare companies when moving to cloud to enable data science - Common cloud data platforms for various Healthcare operations - Best practices for productionizing ML using data frameworks at scale (Spark, Tensorflow, and more) - Real world Life Sciences example using deep learning to predict molecular activity Webinar Abstract: In this webinar, we will be discussing how healthcare companies are modernizing their data platforms and using cloud to help break down data silos enabling innovation with data science. We will cover common cloud operations for ML and AI use cases in healthcare, highlighting several examples in different domains (pharma, life sciences, biotech, and provider services). We will share best practices of proper security and governance when migrating from on-prem to cloud data lake and the value it is helping drive for Qubole customers. Ending with a real-world deep learning example using Merck’s Kaggle competition dataset; where we leverage Tensorflow, Keras, and automated Spark cluster on Qubole’s Notebook to predict molecular activity from numeric descriptors of chemical structure. Ojas Mulay, Solutions Architect, Qubole Ojas is a Solutions Architect with over 12 years of extensive experience in modeling, developing IT architecture & software for critical business applications in life science industry. Prior to Qubole, Ojas spent 9 years at Amgen implementing large scale data solutions across the technology stack leveraging AWS & Azure cloud platforms. In his current role at Qubole, Ojas works with various life science enterprises and helps strategize migration to cloud and derive value from building AI & Machine learning applications to achieve business outcomes. Pradeep Reddy, Solutions Architect, Qubole Pradeep is a strategic and focused technologist, capable of leading, architecting, and delivering enterprise applications ranging from client-server/web applications to Analytics/BI & Data Science Applications. Reddy has experience with client/server technologies, Java EE, SOA, MoM; Big Data Technologies (Hadoop, Hive, Spark, and Presto) as well as BI Tools & Solutions (Tableau, Alteryx, Looker, PowerBI & Cognos). Reddy’s unique skills are in expertise he brings in all facets of Enterprise Architecture to Big Data & Data Science Application Architecture, with a proven track record of execution that delivered business value.