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World Congress 2024 β€’ Aug 20, 2024 β€’ Session details

Supercharge your cloud-native applications with Generative AI

Cedric Clyburn

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

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#1 about 3 min

Integrating generative AI into cloud-native applications

Developers can use popular open source tools to incorporate generative large language models into containerized workflows.

#2 about 4 min

Planning the enterprise AI journey and architecture

Identifying the right model and pipeline design is critical for balancing operational costs with business needs.

#3 about 1 min

Running generative AI models in local environments

Hosting models natively removes latency and authorization blockers while ensuring privacy for confidential business data.

#4 about 4 min

Building local containerized models with Podman AI Lab

Developers can seamlessly spin up open source models and playground environments using an extension for Podman Desktop.

#5 about 4 min

Prototyping a local Python chatbot with LangChain

Using Streamlit and LangChain with local model endpoints allows rapid prototyping of text and object detection systems.

#6 about 3 min

Integrating local AI models into Java Quarkus applications

Java applications leveraging LangChain4J and WebSockets easily retrieve contextual information from local language models.

#7 about 2 min

Expanding AI capabilities using retrieval-augmented generation

Prompt engineering against vector databases supplies foundational models with contextually rich enterprise data.

#8 about 4 min

Transitioning AI workflows into structured production environments

Scaling local workflows to production requires integrating data pipelines, storage platforms, and serverless distribution across Kubernetes clusters.

#9 about 3 min

Processing enterprise documentation into robust vector databases

Chunking technical PDFs using Python scripts automates index creation inside elasticsearch for dynamic content retrieval.

#10 about 5 min

Modifying chatbot code to support vector database retrieval

Injecting elasticsearch bindings and custom embeddings logic connects base prompt templates to external knowledge repositories.

#11 about 3 min

Deploying the augmented chatbot on OpenShift AI platforms

Deploying containerized RAG applications via OpenShift establishes serverless execution environments with fast, contextual text retrieval.

#12 about 2 min

Exploring open source AI resources and learning paths

Developers can access local sandboxes and open source AI toolkits to construct and test intelligent application frameworks.

Matching moments

3:36 min

Overview of generative AI and the presentation agenda

juarezjunior juarezjunior Β· World Congress 2024

3:16 min

Simplifying generative AI adoption with Podman AI Lab

Stevan Le Meur Stevan Le Meur Β· World Congress 2024

3:13 min

Embedding generative AI in enterprise software platforms

Mike Butcher Mike Butcher +3 Β· World Congress 2024

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 Β· World Congress 2024

3:33 min

Crucial lessons for deploying generative AI in enterprises

Alexander Trusheim Alexander Trusheim +1 Β· World Congress 2025

54 sec

Overview of enterprise Java and generative AI

Timo Salm Timo Salm Β· World Congress 2025

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