Machine Learning Essentials for Java Developers

"One of the most interesting aspects of the world is that it can be considered to be made up of patterns," Norbert Wiener (1948).
Today, we find ourselves in a new and long-tail phase of software development with Machine Learning (ML). ML, a subset of AI, can predict or generate results for specific problems without traditional Java programming by analyzing large datasets and identifying patterns. ML can generate code, allow you to query unstructured enterprise docs, summarize emails, offer bug triage strategies, identify inconsistencies in a library, etc.

It is an exciting time and an opportunity to increase your productivity significantly. However, it is also potentially scary for many developers and enterprises.

This session will explain the basics of AI/ML, GenAI vs PredAI, prompt "engineering" strategies, APIs, chatbot architecture, vector databases, and RAG strategies. Plus, we'll explore the OpenAI REST-based API, share some advanced prompt techniques, and discuss how to work with an LLM. Whether you're a newbie or a seasoned Java developer, I'm sure you'll be interested in seeing code demos that illustrate where you can use GenAI and where you should not.

AI/ML represents a megatrend that affects our applications, software tools, data structures, systems architecture, new hardware approaches, business processes, organizational interactions, enterprise strategies, government behavior, geopolitical strengths, ethics, data privacy, etc. There is no exaggeration that ML is an inflection point for computing, enterprises, countries, humanity, and civilization.