Conspect Show list

  • Module 1: Foundation Model Selection and Configuration
    • Enterprise foundation model evaluation framework
    • Dynamic model selection architecture patterns
    • Resilient foundation model system designs
    • Cost optimization and economic modeling
  • Module 2: Advanced Data Processing for Foundation Models
    • Comprehensive data validation and quality assurance
    • Multi-modal data processing pipelines
    • Input optimization and performance enhancement
  • Module 3: Vector Databases and Retrieval Augmentation
    • Enterprise vector database architecture
    • Advanced document processing and chunking strategies
    • Sophisticated retrieval system implementation
    • Hands-on Lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases
  • Module 4: Prompt Engineering and Governance
    • Advanced prompt engineering frameworks
    • Complex prompt orchestration systems
    • Enterprise prompt governance and management
    • Hands-on Lab: Develop conversation pattern with Amazon Bedrock APIs
  • Module 5: Agentic AI and Tool Integration
    • Agentic AI architecture and evolution
    • Amazon Bedrock Agents implementation
    • AWS Agentic AI service ecosystem
    • Tool integration and production observability
  • Module 6: AI Safety and Security
    • Comprehensive content safety implementation
    • Privacy-preserving AI architecture
    • AI governance and compliance frameworks
  • Module 7: Performance Optimization and Cost Management
    • Token efficiency and cost optimization
    • High-performance system architecture
    • Intelligent caching systems implementation
    • Hands-on Lab: Building Secure and Responsible Gen AI with Guardrails for Amazon Bedrock
  • Module 8: Monitoring and Observability for Generative AI
    • Foundation model monitoring systems
    • Business impact and value management
    • AI-specific troubleshooting and diagnostics
  • Module 9: Testing, Validation, and Continuous Improvement
    • Comprehensive AI evaluation frameworks
    • Quality assurance and continuous improvement
    • RAG system evaluation and optimization
  • Module 10: Enterprise Integration Patterns
    • Enterprise connectivity and integration architecture
    • Secure access and identity management
    • Cross-environment and hybrid deployments
  • Module 11: Course wrap-up
    • Next steps and additional resources
    • Course summary
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Additional information

Prerequisites

We recommend that attendees of this course have:

  • AWS Technical Essentials
  • Generative AI Essentials on AWS
  • 2 or more years of experience building production grade applications on AWS or with opensource technologies, general AI/ML or data engineering experience
  • 1 year of hands-on experience implementing generative AI solutions
Difficulty level
Duration 3 days
Certificate

The participants will obtain certificates signed by AWS (course completion).
Trainer

AWS Authorized Instructor (AAI)

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