GenAI on AWS

You want to add AI/GenAI to your product. But the options are overwhelming — Bedrock vs SageMaker vs OpenAI? How do you manage costs? What about latency?

In this session, I'll help you:

✓ Choose the right AWS AI services for your use case

✓ Design cost-effective LLM infrastructure

✓ Avoid common GenAI pitfalls (token costs, hallucinations, latency)

✓ Build RAG pipelines, AI agents, or custom workflows

✓ Production considerations — monitoring, fallbacks, scaling

Topics we can cover:

• Amazon Bedrock (Claude, Titan, Llama models)

• SageMaker for custom model hosting

• Building AI agents with Lambda + Bedrock

• RAG architecture with OpenSearch or Kendra

• Prompt engineering for infrastructure tasks

• Cost optimization for LLM workloads

What to prepare:

• Your use case or product idea

• Current architecture (if any)

• Specific questions

Who this is for:

• Engineers adding GenAI features to products

• Architects designing LLM infrastructure

• Teams evaluating AWS AI services

Currently training LLMs on AWS infrastructure at Turing. Let's build your AI system right.