OK, But What About Predictive AI?
While the tech world is buzzing about generative AI and large language models (LLMs), it’s easy to forget that predictive AI has been, and continues to be, the real engine behind most AI-powered applications. From fraud detection to demand forecasting, predictive models are embedded in nearly every industry.
This talk takes a fresh look at the state of predictive AI in the Java ecosystem. We’ll explore how traditional machine learning is still very much alive, practical, and evolving, especially with recent improvements in model efficiency, portability, and tooling.
You’ll see real-world examples of predictive AI in action and discover some of the Java libraries, frameworks, and platforms that make it all work, from model training to serving. Whether you’re new to AI or looking to balance your GenAI hype with some grounded, production-ready solutions, this session will reconnect you with the part of AI that’s still doing the heavy lifting.
Brayan Muñoz V.
Brayan Muñoz V., an Oracle Ace Associate, is a Java SDE, Data Scientist, and Professor with an MSc in Data Science (UCJC) and a Telematics Engineer degree (PUCMM). He brings six years of software development experience, which includes three years in AI and data science projects, in monolithic and microservices architectures. He currently serves as a Professor and thesis advisor at PUCMM’s School of Computing and Telecommunications. Brayan is a Board Member of the Dominican Republic Java User Group (@JavaDominicano) and a winner of the JCP Program Awards Java in Education category with the group, frequently speaking on Java and AI/ML topics. He is also an open-source enthusiast.
José Rafael Almonte Cabrera
José Rafael Almonte Cabrera is a Telematics and Software Engineer with a Master’s degree in Data Science (UCJC) and 4 years of experience building solutions in FinTech and E-learning platforms. He serves as a Board Member of the Dominican Republic Java User Group (@JavaDominicano). His work is characterized by a strong sense of responsibility, collaboration, and continuous learning, and he is particularly focused on Software Engineering, AI/ML, Data Science, Sports Analytics, and Finance.Outside of work, he enjoys traveling, exploring new cultures, following major sports leagues, experimenting with new recipes, reading, and practicing piano.
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