DataBuilding with Open Source AI: A Crash Course
Thanks to open source, in the past year, we’ve seen a fundamental change: developers and enterprises are moving away from proprietary, closed-source models. To save costs, prioritize privacy, and allow for customization, they are building, testing, and deploying their own open models. However, this journey can feel overwhelming. Which foundation model should I use? How do I connect my model to existing data sources or build agentic capabilities to start seeing real value with AI, especially in an already existing Java application?
The key to navigating this emerging path is adopting the flexibility, transparency, and collaboration of open source that many of us are familiar with. We'll walk through the critical aspects of AI feature implementation using LangChain4J, also showing observability (OpenTelemetry), testing (Promptfoo), CI/CD (Tekton) and more. Join us as we get hands-on with language models and use open technologies to control our own AI journey!
The key to navigating this emerging path is adopting the flexibility, transparency, and collaboration of open source that many of us are familiar with. We'll walk through the critical aspects of AI feature implementation using LangChain4J, also showing observability (OpenTelemetry), testing (Promptfoo), CI/CD (Tekton) and more. Join us as we get hands-on with language models and use open technologies to control our own AI journey!
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