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AWS AI in Practice #6

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What to expect

AWS AI in Practice #6 brings engineers and tech leaders to AutogenAI in London on 26 August 2026 for practical talks on shipping AI reliably on AWS.

Hosted by AWS User Group UK, this two-hour, in-person meetup focuses on the infrastructure, architecture and evaluation decisions behind production AI systems. Rather than high-level predictions, the speakers will share real-world lessons, technical patterns and trade-offs that attendees can apply within their own teams.

Distributed model training on AWS

Anton Nazaruk, CTO at Cloud Combinator, and Ivaylo Iliev, Partner Solutions Architect at AWS, will present “From Zero to HyperPod: Choosing, Operating, and Cost-Controlling Distributed Model Training Infrastructure on AWS.”

The session offers a practical framework for choosing between EC2 On-Demand, Spot, Savings Plans, Capacity Blocks, SageMaker Training Jobs, SageMaker Training Plans and SageMaker HyperPod. It will examine GPU availability, quota planning, interruptions and runaway costs before mapping out a repeatable training architecture covering:

  • Compute fleets and EFA networking
  • FSx and S3 storage
  • Slurm or EKS orchestration
  • Observability and checkpointing
  • Node failure, replacement and job recovery

A pre-recorded walkthrough of a real distributed training run will show how a production-ready setup handles node failure and restores the job. The goal is to make reliability, capacity and cost control architectural considerations from day one.

Intelligent document processing

Silvia Lehnis, Chief AI Officer at UBDS Digital, will share findings from testing an intelligent document processing solution built on Amazon Bedrock. Her team evaluated prompting, model selection, document handling and validation to establish what genuinely improves extraction accuracy across messy, real-world documents.

Expect a candid comparison of methods that deliver value, approaches that do not earn their keep, and the trade-offs between improved results and implementation effort.

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