AI TRAINING
AWS Bedrock for Multi-Model Deployments
Build, deploy, and govern production-grade generative AI workloads on AWS Bedrock using multiple foundation models.
What it covers
This hands-on practitioner programme equips AWS-centric engineering teams with the skills to select, configure, and deploy foundation models from Anthropic, Meta, Cohere, and Mistral via AWS Bedrock. Participants learn to build retrieval-augmented generation pipelines using Bedrock Knowledge Bases, design multi-step autonomous workflows with Bedrock Agents, and enforce safety policies using Guardrails. The programme combines live coding labs, architecture reviews, and real-world deployment scenarios to ensure teams leave with working, production-ready implementations.
What you'll be able to do
- Invoke and compare multiple Bedrock foundation models programmatically using Boto3 and evaluate them against latency, cost, and quality benchmarks
- Build a production-ready RAG application using Bedrock Knowledge Bases with an OpenSearch vector store and custom chunking strategies
- Design and deploy a Bedrock Agent with custom action groups that integrates external APIs and executes multi-step reasoning tasks
- Configure Bedrock Guardrails to enforce content filters, PII redaction, and topic restrictions across all deployed models
- Apply IAM least-privilege policies, VPC endpoint configurations, and cost controls to a secure, compliant Bedrock deployment
Topics covered
- Bedrock model catalogue: Anthropic Claude, Meta Llama, Cohere Command, Mistral — selection criteria and trade-offs
- Invoking foundation models via the Bedrock API and AWS SDK (Python/Boto3)
- Building RAG pipelines with Bedrock Knowledge Bases and Amazon OpenSearch
- Designing multi-step agentic workflows with Bedrock Agents and custom action groups
- Enforcing content and safety policies with Bedrock Guardrails
- Fine-tuning and continued pre-training workflows on Bedrock
- IAM roles, VPC endpoints, and security best practices for Bedrock deployments
- Cost monitoring, token budgeting, and latency optimisation across models
Delivery
Delivered as a 3-day intensive bootcamp (on-site or virtual instructor-led). Approximately 60% of time is hands-on lab work within participants' own AWS accounts or a provided sandbox environment. Participants receive a pre-configured AWS CloudFormation template, a GitHub repository of reference architectures, and a post-bootcamp architecture review session. Remote delivery uses Zoom or Teams with shared VS Code Server environments. A follow-up office-hours session (2 hours) is included two weeks after delivery.
What makes it work
- Teams bring a real internal use case to the bootcamp and build against it rather than working on toy examples
- A dedicated AWS account with Bedrock access is provisioned before day one to avoid lab delays
- Security and compliance stakeholders join at least the Guardrails and IAM module to align on governance requirements from the start
- Post-bootcamp, teams nominate a Bedrock internal champion responsible for maintaining the reference architecture and onboarding future colleagues
Common mistakes
- Defaulting to a single model (usually Claude) without establishing a model selection framework, leading to cost and capability mismatches later
- Building RAG pipelines without tuning chunking strategies or embedding models, resulting in poor retrieval relevance in production
- Skipping Guardrails configuration until post-launch, causing compliance and reputational incidents
- Over-provisioning model throughput units (MTUs) without understanding Bedrock's provisioned vs on-demand pricing trade-offs
When NOT to take this
This bootcamp is not appropriate for teams that have not yet chosen AWS as their cloud provider or whose organisation is still evaluating whether to build vs buy generative AI capabilities — a cloud-agnostic AI strategy workshop should come first.
Providers to consider
Sources
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