Erik Bamberg
AI Principal Software Engineer at Kodamai | Public Speaker | Advisor | Java Expert and Trainer
Glasgow, United Kingdom
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AI Principal Software Engineer with extensive Java expertise, system design, and project management experience.
Specialized in AI/ML technologies, Large Language Models (LLMs), and Vector databases.
Java Expert and Trainer and Mentor. Strategic technical
advisor driving innovation in AI-powered software solutions
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AI Agents in LegalTech: Lessons learned and challenges
This session pulls back the curtain on a LegalTech project for document management—and learned some lessons along the way.
You'll discover the challenges we faced and the solutions we implemented, including:
Technical Hurdles:
Processing huge volumes of extremely large documents
Scaling infrastructure to support multiple concurrent users
Optimizing document processing performance
Human Challenges:
Bridging the gap between legal professionals' expectations and AI capabilities
Navigating product requirements from managers unfamiliar with AI limitations
Building robust evaluation frameworks (spoiler: LLMs won't validate their own outputs)
Practical Takeaways:
Who should attend: Software developers, architects, and technical leads who understand IT fundamentals but want practical guidance on implementing AI systems in production environments. No deep AI expertise required—just curiosity and a healthy skepticism,
Integration AI Agents in Your Java-World with Spring AI
Java remains the backbone of enterprise applications across countless organizations worldwide, with development teams possessing deep expertise and established practices in the language. As artificial intelligence and LLM technologies rapidly evolve, Java developers no longer need to venture outside their ecosystem or learn Python to build sophisticated AI-powered applications. Spring AI emerges as a comprehensive, feature-rich framework that brings the power of large language models and AI agents directly into the Java development environment. This framework provides all the essential tools and capabilities needed for common AI applications—from prompt engineering and model integration to vector databases and retrieval-augmented generation. By leveraging Spring AI, your existing Java teams can seamlessly integrate intelligent agents into enterprise systems, maintaining the robustness, scalability, and maintainability that Spring framework users expect. This session explores how to harness Spring AI to build production-ready AI agents without leaving the familiar Java and Spring ecosystem, empowering your developers to innovate with AI using the skills and knowledge they already possess.
What comes after ChatGPT? Vector Databases - the Simple and powerful future of ML?
What comes after ChatGPT? Vector database projects like Weaviate, Pinecone, and Chroma recently got millions of dollars of funding for their projects. But what are vector databases? And why will they be so important in the future?
Let us see how Vector Databases can help you define and run your machine learning business use cases. We will explore some real-world use cases and try to understand the potential of vectors and vector databases.
Not exactly sure what a vector is ? No worries, you will learn everything you need about vectors .
A brief hands-on demonstration just using open source will give you an idea, of how to use the new generation of databases in praxis.
We will also cover how vector databases can work together with chatGPT and helps you to overcome some limitations of chatGPT.
I/O Extended 2023 Sessionize Event
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