Explore enterprise-level deployment of TensorFlow models in Java server environments through this comprehensive conference talk from ML Conference 2017. Discover why running inference on third-party cloud services may not be ideal for certain scenarios, especially in enterprise settings. Learn about integrating machine learning solutions into custom cloud or traditional server infrastructures using Java, the most widespread platform for enterprise systems. Delve into real-world examples of integrating TensorFlow models with popular server frameworks like Spring and Apache CXF. Examine different approaches for deployment and version control of trained models, and understand the challenges and benefits of using TensorFlow in Java Enterprise Server environments. Gain insights into the TensorFlow Persistence API design philosophy, Saved Model structure, and techniques for saving data compatible with other libraries. Master the integration of TensorFlow in Java, including proper use of Java wrappers and essential preprocessing steps. Explore server-side frameworks and queuing strategies to optimize your machine learning deployments in enterprise settings. Read more