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Machine Learning Engineer
The course addresses the needs of organizations embarking on their generative AI journey and how to build comprehensive generative AI strategies that align with broader business objectives.
In the course, you will build expertise across the entire generative AI stack—from foundation models to enterprise integration patterns. In addition, you will learn about advanced data processing techniques, vector database implementation and retrieval augmentation, sophisticated prompt engineering and governance, autonomous AI systems and tool integration, AI safety and security measures, performance optimization and cost management strategies, comprehensive monitoring and observability solutions, testing and validation frameworks.
The course structure follows AWS's proven model for generative AI adoption, progressing from experimentation to production-ready implementations.
Day 1
Module 1: Foundation Model Selection and Configuration
- Enterprise foundation model evaluation framework
- Dynamic model selection architecture patterns
- Resilient foundation model system designs
- Optimization of costs and economic modeling
Module 2: Advanced Data Processing for Foundation Models
- Comprehensive data validation and quality assurance
- Multimodal data processing pipelines
- Optimization of input and enhancement of performance
Module 3: Vector Databases and Retrieval Augmentation
- Enterprise vector database architecture
- Advanced document processing and chunking strategies
- Implementation of a sophisticated retrieval system
- Hands-on Lab: Develop Retrieval-Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases
Day 2
Module 4: Prompt Engineering and Governance
- Advanced prompt engineering frameworks
- Complex prompt orchestration systems
- Enterprise prompt governance and management
- Hands-on Lab: Develop conversation patterns 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
- Integration of tools and production observability
Module 6: AI Safety and Security
- Comprehensive content safety implementation
- Privacy-preserving AI architecture
- AI governance and compliance frameworks
Day 3
Module 7: Performance Optimization and Cost Management
- Token efficiency and cost optimization
- High-performance system architecture
- Implementation of intelligent caching systems
- 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
- Impact commercial et gestion de la valeur
- AI-specific troubleshooting and diagnostics
Module 9: Testing, Validation, and Continuous Improvement
- Comprehensive AI evaluation frameworks
- Quality assurance and continuous improvement
- Evaluation and optimization of RAG systems
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
Requirements
- 2 or more years of experience building production-grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience
- 1 year of hands-on experience implementing generative AI solutions
- Developing production-ready generative AI solutions using AWS services that meet enterprise requirements for security, scalability, and reliability
- Evaluating and selecting appropriate foundation models for specific business use cases, including benchmarking performance and implementing dynamic model selection architectures
- Designing and implementing resilient foundation model systems with circuit breakers, cross-region deployment, and graceful degradation strategies
- Building comprehensive data processing pipelines for multi-modal inputs, including validation workflows and optimization techniques
- Implementing sophisticated vector database solutions using Amazon Bedrock Knowledge Bases, OpenSearch, and hybrid approaches for effective retrieval augmentation
- Creating and managing advanced prompt engineering frameworks, including chain-of-thought reasoning and enterprise-wide prompt governance systems
- Developing autonomous AI agents using Amazon Bedrock Agents, implementing complex reasoning patterns and tool integration capabilities
- Implementing comprehensive AI safety and security controls, including content filtering, privacy preservation, and adversarial testing mechanisms
- Optimizing performance and managing costs through token efficiency strategies, batching implementations, and intelligent caching systems
- Designing and implementing comprehensive monitoring and observability solutions for foundation model applications
- Creating systematic testing and validation frameworks for continuous quality assurance of AI applications
- Integrating generative AI solutions within enterprise environments using secure, compliant, and scalable architectural patterns
This course includes presentations, hands-on labs, demonstrations, and group exercises.
- Software developers
- Technical professionals
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