While the AI market landscape continues to expand, scaling machine learning—which involves moving past the “AI pilot” phase to build, train, and deploy optimized, performant models that are robust enough for production—remains a significant challenge. This talk presents a high-level, practical overview of overcoming various bottlenecks in the machine-learning lifecycle using Spring Cloud Data Flow and MLFlow. Featuring a hybrid use case that blends conventional machine learning with deep learning, it provides an end-to-end outline of various stages of the machine-learning loop, including model engineering with ML pipelines and model deployment.
SpringOne Essentials Schedule
End-to-End Machine and Deep Learning with MLFlow and Spring