For years, Python has dominated the data science landscape, leaving Java developers to bridge the gap between model experimentation and enterprise production. This session breaks that barrier by demonstrating how to build, train, and deploy high-performance Machine Learning models entirely within the Java ecosystem. We will explore the Tribuo library’s robust architecture, highlighting its unique ability to provide compile-time type safety for ML models.

By the end of the session, you’ll understand how to combine these tools to build robust ML labs that fit naturally into enterprise Java environments while benefiting from the interactivity and clarity of notebook‑based development.