ML on the JVM
summary
The talk examines doing machine learning on the JVM: when and why to use JVM-based ML, challenges of porting ML tools from Python, and what tasks are easier or possible on the JVM compared to Python. It surveys existing JVM ML libraries and gaps where tools are missing, and discusses how ML can assist Java developers and best practices for JVM ML development.
AI-written summary · about
chapters (12)
- 0:00Introduction
- 1:04Goal and relevance
- 5:20ML: training vs inference
- 10:45Model integration challenges
- 18:01Code standardization and migration
- 23:45Python popularity in ML
- 28:32Python alternatives on JVM
- 30:19Team organization and collaboration
- 34:56Future of interdisciplinary roles
- 37:03ML for tool developers
- 41:14AI assistants impact on development
- 45:12Recommendations for listeners
related talks
43mWhy Java and the JVM Will Dominate the Future of Machine Learning, AI, and Big Data
47mML in Java, YES it's possible!
46mML in Java, YES it's possible!
50mML in Java, YES it's possible!
49mJVM in the Age of AI: Babylon, Valhalla, TornadoVM and friends
55mOrganizing the Machine Learning Lifecycle with MLflow
55mML with real impact driven by OS technologies
27mEngineering Scalable AI with Java