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# Supercharge your cloud-native applications with Generative AI

Stop risking enterprise data with hosted GenAI services. Learn to build secure, containerized RAG pipelines locally and effortlessly scale them to Kubernetes.

- **Speakers:** [Cedric Clyburn](https://www.wearedevelopers.com/@cedric-clyburn)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 32:13
- **URL:** https://www.wearedevelopers.com/videos/950-supercharge-your-cloud-native-applications-with-generative-ai

## Summary

Integrating generative AI into cloud-native applications presents challenges regarding API costs, latency, and strict enterprise data privacy mandates. Relying entirely on hosted services limits iteration speed and introduces security risks when handling confidential operational data. The foundation of a secure, flexible enterprise AI strategy starts with localized, containerized development. Developers can bypass deep machine learning complexities by interacting with open-source models via API endpoints, using tools like Podman AI Lab to run foundational infrastructure locally within containers. This approach guarantees data privacy while offering latency-free, cost-effective prototyping through boilerplate recipes for frameworks like LangChain and LangChain4j inside Java Quarkus applications.

While localized models provide solid general reasoning, they initially lack specific organizational knowledge. The retrieval-augmented generation (RAG) pattern serves as a highly cost-effective alternative to fine-tuning, dramatically enhancing a model's contextual awareness without retraining overhead. By embedding technical documentation or private records into a vector database like Elasticsearch, applications can directly inject verified enterprise data into the generation loop. This pipeline chunks source material and applies a similarity score against user prompts, ensuring that chatbot responses are grounded in accurate, secure company information.

Scaling these localized AI features into a production-ready Kubernetes environment requires consistent operational practices and robust data integration. Utilizing complete MLOps distributions, such as OpenShift AI, simplifies this transition by packaging critical dependencies—like model serving infrastructure, Jupyter notebook integrations, and vector search operations—into automated, scalable deployments. Maintaining the exact same containerized stack from local development to remote clusters ensures predictability and lifecycle stability. Ultimately, developers can focus on writing resilient business logic while relying on existing orchestration infrastructure to handle dynamic scaling, serverless request execution, and complex RAG data ingestion pipelines.

**Keywords:** cloud-native ai applications, generative ai containers, podman ai lab, local language model inference, kubernetes model deployment, retrieval-augmented generation, enterprise private data rag, openshift ai platform, langchain java integration, quarkus ai development, elasticsearch vector database, hugging face model serving, secure local ai prototyping, open source mlops stack

## Chapters

1. **Integrating generative AI into cloud-native applications** (00:19) — Developers can use popular open source tools to incorporate generative large language models into containerized workflows.
1. **Planning the enterprise AI journey and architecture** (02:51) — Identifying the right model and pipeline design is critical for balancing operational costs with business needs.
1. **Running generative AI models in local environments** (06:08) — Hosting models natively removes latency and authorization blockers while ensuring privacy for confidential business data.
1. **Building local containerized models with Podman AI Lab** (07:03) — Developers can seamlessly spin up open source models and playground environments using an extension for Podman Desktop.
1. **Prototyping a local Python chatbot with LangChain** (10:15) — Using Streamlit and LangChain with local model endpoints allows rapid prototyping of text and object detection systems.
1. **Integrating local AI models into Java Quarkus applications** (13:44) — Java applications leveraging LangChain4J and WebSockets easily retrieve contextual information from local language models.
1. **Expanding AI capabilities using retrieval-augmented generation** (16:41) — Prompt engineering against vector databases supplies foundational models with contextually rich enterprise data.
1. **Transitioning AI workflows into structured production environments** (18:06) — Scaling local workflows to production requires integrating data pipelines, storage platforms, and serverless distribution across Kubernetes clusters.
1. **Processing enterprise documentation into robust vector databases** (21:23) — Chunking technical PDFs using Python scripts automates index creation inside elasticsearch for dynamic content retrieval.
1. **Modifying chatbot code to support vector database retrieval** (23:59) — Injecting elasticsearch bindings and custom embeddings logic connects base prompt templates to external knowledge repositories.
1. **Deploying the augmented chatbot on OpenShift AI platforms** (28:14) — Deploying containerized RAG applications via OpenShift establishes serverless execution environments with fast, contextual text retrieval.
1. **Exploring open source AI resources and learning paths** (30:52) — Developers can access local sandboxes and open source AI toolkits to construct and test intelligent application frameworks.

## Related Moments

- [Overview of generative AI and the presentation agenda](https://www.wearedevelopers.com/videos/1001-langchain4j-an-introduction-for-impatient-developers) (from "Langchain4J - An Introduction for Impatient Developers")
- [Simplifying generative AI adoption with Podman AI Lab](https://www.wearedevelopers.com/videos/1133-containers-and-kubernetes-made-easy-deep-dive-into-podman-desktop-and-new-ai-capabilities) (from "Containers and Kubernetes made easy: Deep dive into Podman Desktop and new AI capabilities")
- [Embedding generative AI in enterprise software platforms](https://www.wearedevelopers.com/videos/916-beyond-the-hype-real-world-ai-strategies-panel) (from "Beyond the Hype: Real-World AI Strategies Panel")
- [Scaling generative AI use cases across large enterprises](https://www.wearedevelopers.com/videos/916-beyond-the-hype-real-world-ai-strategies-panel) (from "Beyond the Hype: Real-World AI Strategies Panel")
- [Crucial lessons for deploying generative AI in enterprises](https://www.wearedevelopers.com/videos/1546-ai-pair-programming-with-github-copilot-at-sap-looking-back-looking-forward) (from "AI Pair Programming with GitHub Copilot at SAP: Looking Back, Looking Forward!")
- [Overview of enterprise Java and generative AI](https://www.wearedevelopers.com/videos/1554-java-meets-ai-empowering-spring-developers-to-build-intelligent-apps) (from "Java Meets AI: Empowering Spring Developers to Build Intelligent Apps")

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