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Concerto for Java and AI - Building Production-...

Concerto for Java and AI - Building Production-Ready LLM Applications

Imagine you’re a music composer struggling to find inspiration for a pivotal movie scene. Then, you remember you’re also a software engineer, and the solution becomes suddenly obvious. Join me in this session, where I’ll demonstrate how I enhanced my music composition process by harnessing the power of Spring Boot and AI.

This presentation will discuss the core architectural patterns for introducing AI capabilities into an existing software system, exploring concepts like templated prompts, agent tools, and integration of external knowledge. The Java ecosystem is getting more and more capabilities for building AI applications. But are they ready for production? Are there any gaps?

Throughout the session, I’ll build a “composer assistant” application using Spring Boot and an AI Orchestrator (Spring AI) to showcase how to make an LLM application production-ready. Is the developer experience affected when working locally with language models? How is observability different when it comes to tokens? Can we ensure resilience across the many integrations orchestrated by the AI? What strategies are available for deploying LLM applications?

In a final twist, you’ll choose which movie scene to score, and I’ll compose and perform the music live for it, supported by AI. Will it meet the mark? There’s only one way to find out: join me in exploring the practical side of AI applications, where Java and Generative AI offer tangible solutions to real-world use cases. Aaaaand action!

Thomas Vitale

June 04, 2024
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  1. Thomas Vitale Spring I/O May 31st, 2024 Concerto for Java

    and AI Building Production-Ready LLM Applications @vitalethomas
  2. Systematic • Software Engineer • CNCF Ambassador, Oracle ACE Pro,

    Testcontainers Community Champion • Author of “Cloud Native Spring in Action” (Manning). • OSS contributor (Java, Spring, Cloud Native Technologies) Thomas Vitale thomasvitale.com @vitalethomas
  3. The WHY Factor @vitalethomas What problem does it solve? How

    ready is it for production? Yeah, but how about the DevEx?
  4. Machine Learning Subset of Arti fi cial Intelligence @vitalethomas Model

    Training Model Fine-Tuning Model Inference ML Engineers Platform/Infrastructure Platform Engineers HTTP API Application Developer
  5. Model Inference via HTTP APIs Spring AI @vitalethomas Application Model

    Inference Service Do you wanna build a snowman? HTTP Spring Data Application Database Service DELETE * FROM HYPE; JDBC
  6. LLM Security Risks (1) OWASP Top 10 for LLM Prompt

    Injection Model Denial of Service Sensitive Information Disclosure @vitalethomas OWASP Top 10 LLM Applications and Generative AI https://genai.owasp.org/
  7. Semantic Search @vitalethomas From Keywords to Meaning Application Melancholic Embedding

    Model Melancholic [42…] LIKE ‘%melancholic%' Database Vector Store [42…]
  8. Question Answering with Docs @vitalethomas Retrieval Augmented Generation Application Melancholic

    instrument? Embedding Model Melancholic instrument? [42…] Get Similar Documents Vector Database Model Service Question + Similar Documents
  9. Structured Data Extraction @vitalethomas From Text to JSON Application Text

    Text to Structured JSON Model Service Database Save Structured JSON
  10. LLM Security Risks (2) OWASP Top 10 for LLM Insecure

    Output Handling Excessive Agency Insecure Plugin Design @vitalethomas OWASP Top 10 LLM Applications and Generative AI https://genai.owasp.org/
  11. Data Validation JSON Schema Humans in the Loop Optional Values

    @vitalethomas Mitigating hallucination risks
  12. Speech Transcription @vitalethomas From Speech to Text Application Audio Audio

    to Text Audio Model Chat Model Text to Structured JSON
  13. Build & Deploy Cloud Native Buildpacks Kubernetes Service Binding Native

    Executables with GraalVM @vitalethomas Going to Production