This hands-on session is designed for ML enthusiasts and biostatistics students who are familiar with the theory of ML but new to the world of operationalising and production deployment. Spanning healthcare and life sciences use cases, this workshop bridges the gap between experimentation and real-world impact.

Participants will learn the full ML lifecycle—from model development to deployment, monitoring, and feedback—through relatable examples and simplified end-to-end workflows. The session includes:

• Walkthroughs of lightweight production pipelines
• Introductions to essential tools (FastAPI, Docker, MLflow)
• Group exercises to help learners think like ML engineers

By the end, attendees will understand how to move from model building to creating scalable, maintainable, and production-ready ML systems that work in real-world conditions.

• Healthcare, BioStats students, and early-career researchers
• Data scientists and ML practitioners transitioning from notebooks to deployment
• Anyone experienced in ML modeling wanting to understand the operational side

Sushant Penshanwar : Sushant Penshanwar is a Senior Data Scientist at ReliaQuest, a leading cybersecurity company, where they focus on applying AI to enhance threat detection and decision automation. With nearly a decade of experience across global organizations like General Motors, Pfizer, and Cipla, their expertise spans Natural Language Processing, Classical Machine Learning, Generative AI, and Agentic AI.

During their tenure at Pfizer, Sushant led impactful research on biomarker selection for multiple myeloma using machine learning — a project that deepened their commitment to applying AI in high-stakes, real-world domains. Currently, they are focused on the practical challenges of taking machine learning models from experimentation to scalable, production-ready systems.

Sushant will be speaking on the practical frameworks and challenges of productionizing ML, drawing from their diverse cross-industry experience.