Amazon Bedrock Training

Master AWS’s Managed Multi-Model AI Platform

Build Secure, Scalable AI Applications with Enterprise-Grade Features

Learn to leverage Amazon Bedrock’s powerful multi-model platform to build production AI applications. Access leading models like Claude 3, LLaMA 4, and Titan with built-in security and compliance.

🎯 Course Overview

This intensive 2-day course covers Amazon Bedrock’s complete ecosystem, from model selection to production deployment. Master serverless AI with enterprise security, compliance, and seamless AWS integration.

What You’ll Master

• 🤖 Multi-Model Access: Claude 3, Cohere Command R+, LLaMA 4, Titan
• 🔍 RAG Workflows: Knowledge bases and real-time retrieval
• 🛡️ Enterprise Security: Region-based privacy and compliance
• 🔗 AWS Integration: Lambda, S3, DynamoDB, and more
• 📊 Cost Optimization: Model selection and usage strategies

Who Should Attend

• AWS developers adding AI capabilities
• Architects designing secure AI systems
• DevOps teams managing AI infrastructure
• Security teams evaluating AI platforms
• Anyone building AI on AWS

📚 Detailed Curriculum

Day 1: Bedrock Fundamentals & Model Mastery

Morning Session: Platform Overview & Setup

• Amazon Bedrock Architecture

  • Serverless AI benefits
  • Model marketplace overview
  • Security and compliance features
  • Pricing models and optimization

• Available Models Deep Dive

  • Claude 3: Advanced reasoning and analysis
  • LLaMA 4: Open-source powerhouse
  • Amazon Titan: Embeddings and generation
  • Cohere Command R+: Enterprise search and RAG
  • Stable Diffusion: Image generation

• Hands-On Lab 1: Model Exploration

  • Set up Bedrock access
  • Test different models
  • Compare performance and costs
  • Implement basic applications

Afternoon Session: Advanced Model Usage

• Prompt Engineering for Each Model

  • Model-specific optimizations
  • Cross-model prompt portability
  • System prompts and parameters
  • Output format control

• Model Chaining & Orchestration

  • Sequential model calls
  • Parallel processing patterns
  • Error handling strategies
  • Response validation

• Hands-On Lab 2: Multi-Model Application

  • Build application using 3+ models
  • Implement model routing logic
  • Add fallback mechanisms
  • Optimize for cost and performance

Day 2: RAG, Security & Production Deployment

Morning Session: Knowledge Bases & RAG

• Bedrock Knowledge Bases

  • Data source configuration
  • Ingestion pipelines
  • Vector storage options
  • Metadata management

• RAG Implementation

  • Retrieval strategies
  • Context window optimization
  • Citation tracking
  • Accuracy improvement

• Hands-On Lab 3: Production RAG System

  • Create knowledge base
  • Configure data sources
  • Implement RAG pipeline
  • Add source attribution

Afternoon Session: Security & Deployment

• Enterprise Security Features

  • VPC endpoints configuration
  • IAM policies and roles
  • Data privacy controls
  • Audit logging setup

• Production Deployment

  • Lambda integration patterns
  • API Gateway setup
  • Step Functions orchestration
  • CloudWatch monitoring

• Hands-On Lab 4: Secure Production System

  • Deploy serverless AI API
  • Implement authentication
  • Set up monitoring
  • Configure auto-scaling

🛠️ Real-World Projects

Project 1: Intelligent Document Processor

Build a system that:

• Ingests multiple document formats
• Extracts and validates information
• Answers questions about content
• Maintains compliance requirements

Project 2: Multi-Model Customer Service

Create an AI platform that:

• Routes queries to appropriate models
• Handles text and image inputs
• Integrates with existing systems
• Tracks conversation context

Project 3: Serverless AI API

Develop a production API that:

• Exposes multiple AI capabilities
• Handles authentication and quotas
• Implements cost controls
• Scales automatically

💡 Advanced Topics Covered

Fine-Tuning & Customization

• Custom model deployment
• Fine-tuning workflows
• Continued pre-training
• Model evaluation metrics

Cost Management

• Token usage optimization
• Model selection strategies
• Caching implementations
• Budget alerts and controls

Compliance & Governance

• HIPAA compliance patterns
• GDPR considerations
• Data residency controls
• Audit trail implementation

Integration Patterns

• EventBridge integration
• SQS/SNS patterns
• Kinesis streaming
• ECS/EKS deployment

📋 Prerequisites

Required Knowledge

• AWS fundamentals (IAM, Lambda, S3)
• Python or JavaScript programming
• Basic API concepts
• Command line usage

Recommended Experience

• AWS Solutions Architect Associate level
• RESTful API development
• Serverless architecture basics

Technical Requirements

• AWS account with Bedrock access
• AWS CLI configured
• Python 3.8+ or Node.js 16+
• VS Code or preferred IDE

💰 Pricing & Options

Training Formats

On-Site Training

• Price: $12,000 for up to 15 participants
• Duration: 2 consecutive days
• Includes: Custom use cases and AWS architecture review
• Bonus: 1-month implementation support

Virtual Training

• Price: $8,000 for up to 15 participants
• Duration: 2 days (6 hours per day)
• Format: Live online with hands-on labs
• Support: 30-day Q&A access

Public Classes

• Price: $1,495 per participant
• Schedule: Bi-weekly offerings
• Locations: Major cities + online
• Next Session: View Calendar

What’s Included

• Complete course materials
• $200 AWS credits per participant
• Production code templates
• Bedrock best practices guide
• Certificate of completion
• Alumni community access

🎯 Learning Outcomes

After this training, you will:

✅ Select optimal models for any use case
✅ Build secure, compliant AI applications
✅ Implement production RAG systems
✅ Integrate Bedrock with AWS services
✅ Optimize costs while maintaining quality
✅ Deploy serverless AI at scale
✅ Handle enterprise security requirements
✅ Monitor and maintain AI systems

👨‍🏫 Expert Instructors

Learn from AWS-certified AI specialists:

• AWS expertise: Multiple certifications, real deployments
• Bedrock experience: Production systems serving millions
• Security focus: Enterprise compliance implementations
• Continuous updates: Direct line to AWS product teams

🚀 Start Building on Bedrock

Join the Future of Serverless AI

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📚 Resources & Materials

Pre-Course Preparation

• Bedrock Getting Started Guide
• Model Comparison Chart
• AWS AI Services Overview
• Cost Calculator

Ongoing Support

• 30-day instructor access
• Private Slack channel
• Monthly webinars
• Feature update notifications

Related Training

• AWS SageMaker Training
• Serverless Architecture
• AWS Security Best Practices

❓ Frequently Asked Questions

Q: How does Bedrock compare to SageMaker?
A: Bedrock is for using pre-trained models via API, while SageMaker is for training custom models. We cover when to use each.

Q: Do I need deep ML knowledge?
A: No! Bedrock abstracts the complexity. Basic AWS knowledge and programming skills are sufficient.

Q: Which AWS regions are covered?
A: We cover all Bedrock-enabled regions and discuss data residency strategies.

Q: Can we use our AWS account?
A: Yes, we encourage using your account for realistic cost understanding and setup.

Q: Is this updated for the latest models?
A: Yes, we update within days of new model releases.

View All FAQs →

🏆 What Students Say

🎓 Certification & Beyond

Training completion includes:

• Certificate of completion
• LinkedIn badge
• AWS community recognition
• Project portfolio credit

Prepare for:

• AWS Certified Machine Learning
• AWS Solutions Architect Professional
• AI/ML Specialty certification