When you think about building and/or deploying machine learning models, Java is not within the top 3 languages that we generally think of. Nonetheless Java has a strong community, with millions of developers using it as their main language. With the AI buzz spiking in the past month, All developers, and especially Java developers, need to understand how to build and run apps that use ML. In this session we are going to explore what are our options, as java developers, to build, save and run machine learning models.We are also going to discuss and compare the most 3 popular frameworks (Deeplearning4J, djl and Tribuo), along with a real world example to understand capabilities and tradeoffs of each framework. We will also briefly cover JSR381, an open-source, Java-friendly API for ML, specifically visual recognition

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Mohammed is a community catalyst, a true open-source believer who has contributed to many open-source projects. Mohammed has extensive hands-on, cross-industry experience in designing, building and evolving distributed applications at scale. He's one of Google developer experts in cloud, and work @Spotify as Backend engineer.