Harnessing Generative AI on AWS: A Practical Guide for IT Professionals

Harnessing Generative AI on AWS: A Practical Guide for IT Professionals

Welcome to this intensive one-day seminar on generative AI implementation using AWS services. As generative AI rapidly transforms industries and creates new technological possibilities, IT professionals are increasingly tasked with evaluating, implementing, and supporting these technologies within their organisations. This practical workshop bridges the gap between theoretical AI concepts and real-world implementation on the AWS cloud platform.

Throughout this seminar, you’ll gain hands-on experience with AWS’s powerful generative AI services, particularly AWS Bedrock, and learn how to integrate these capabilities into your existing IT infrastructure. The curriculum balances conceptual understanding with practical application, ensuring you leave with both the knowledge and skills needed to begin implementing generative AI solutions immediately.

We’ll explore the fundamentals of generative AI technology, work directly with foundation models, develop integration patterns that align with IT best practices, and address critical concerns around security, cost management, and governance. By the end of the day, you’ll have completed hands-on labs building actual generative AI solutions and developed a strategic roadmap for implementing these technologies in your organisation.

This seminar is designed to be accessible for those new to AI while still providing substantial technical depth and practical guidance. You’ll leave equipped with the confidence, skills, and resources needed to harness the transformative potential of generative AI on AWS.

Learning Outcomes

By the end of this seminar, participants will be able to:

• Understand core generative AI concepts and their potential applications in IT environments
• Navigate and utilize AWS Bedrock to implement foundation models in practical scenarios
• Apply effective prompt engineering techniques to achieve desired AI outputs
• Develop integration patterns for incorporating generative AI into existing systems and workflows
• Implement basic Retrieval-Augmented Generation (RAG) systems on AWS
• Configure proper security controls and permissions for AWS generative AI services
• Estimate and manage costs associated with generative AI implementations
• Create a structured roadmap for generative AI adoption in their organisation
• Identify appropriate AWS services for different generative AI use cases

Seminar Outline

Module 1: Introduction to Generative AI on AWS (9:00-9:45)

• Understanding the foundations of generative AI and how it differs from traditional AI approaches
• Exploring the evolution and capabilities of Large Language Models (LLMs)
• Navigating AWS’s generative AI service ecosystem and understanding the role of each service
• Identifying practical generative AI use cases relevant to IT departments and operations
• Understanding the technical requirements and infrastructure considerations for AI implementation
• Exploring the business value proposition and ROI considerations for generative AI projects

Module 2: AWS Bedrock Fundamentals (9:45-10:30)

• Understanding AWS Bedrock as a managed service for foundation models
• Exploring available foundation models in Bedrock (Anthropic Claude, Meta Llama, etc.)
• Comparing model capabilities, strengths, and appropriate use cases
• Understanding model parameters and their impact on performance and cost
• Navigating the AWS Bedrock console and API interfaces
• Exploring model inference options and configuration settings

Module 3: Hands-on Lab: First Steps with AWS Bedrock (10:45-12:00)

• Setting up AWS Bedrock access and configuring necessary permissions
• Exploring the AWS Bedrock console and available foundation models
• Implementing effective prompt engineering techniques and best practices
• Creating basic text generation applications using the Bedrock API
• Understanding and adjusting key model parameters (temperature, top-p, tokens)
• Building simple conversational interfaces with foundation models
• Testing and evaluating model outputs across different scenarios

Module 4: AWS GenAI Integration Patterns (1:00-2:00)

• Designing effective architectural patterns for generative AI integration
• Implementing serverless AI solutions using AWS Lambda with Bedrock
• Understanding when to use Amazon SageMaker for custom model training and deployment
• Exploring AWS SDK integration options for different programming languages
• Implementing security best practices for generative AI applications
• Developing effective caching strategies to optimize performance and cost
• Understanding API throttling, quotas, and scaling considerations

Module 5: Hands-on Lab: Building Your First AWS GenAI Solution (2:00-3:15)

• Developing a document analysis system using AWS Bedrock and supporting services
• Implementing Retrieval-Augmented Generation (RAG) with Amazon OpenSearch and Bedrock
• Configuring AWS S3 for efficient document storage and retrieval
• Setting up proper IAM roles and permissions for secure operation
• Building API interfaces to your generative AI solution
• Testing and troubleshooting common integration issues
• Implementing basic monitoring and logging for your application

Module 6: Cost Management & Optimization (3:30-4:15)

• Understanding AWS generative AI pricing models and cost components
• Analyzing the cost implications of different foundation models and parameters
• Implementing architectural patterns to optimize cost efficiency
• Setting up AWS Budgets and cost alerts for generative AI workloads
• Understanding token usage optimization techniques
• Implementing caching strategies to reduce redundant API calls
• Balancing cost, performance, and capability in model selection

Module 7: Implementation Planning & Next Steps (4:15-5:00)

• Developing a framework for identifying high-value generative AI opportunities
• Creating a structured 30-60-90 day implementation roadmap
• Understanding governance considerations for responsible AI deployment
• Exploring strategies for measuring success and demonstrating value
• Navigating available resources for continued learning and development
• Addressing common challenges and pitfalls in generative AI implementation
• Open Q&A session for specific implementation questions

Conclusion

This seminar provides a comprehensive introduction to implementing generative AI solutions on AWS. By combining foundational knowledge with hands-on practice and strategic planning, participants will leave equipped to begin their generative AI journey with confidence.

The field of generative AI is rapidly evolving, with new models and capabilities emerging regularly. The frameworks and implementation patterns covered in this seminar will provide a solid foundation that can adapt to these changes, ensuring your skills remain relevant as the technology advances.

Remember that successful AI implementation is an iterative process that requires ongoing learning and refinement. The resources provided during this seminar will support your continued development as you move from initial exploration to sophisticated AI implementations that deliver real business value.

Intended Audience

This seminar is designed for IT professionals, solution architects, developers, and technical leaders who are new to generative AI but want to understand how to implement it using AWS services. Ideal participants include IT staff responsible for evaluating new technologies, developers interested in adding AI capabilities to applications, and technical decision-makers planning AI strategy. The content is tailored for those with AWS experience who want to expand their skills into the AI domain.

Prerequisites

Those attending this course should meet the following:

• Basic familiarity with AWS services and console
• Understanding of fundamental cloud computing concepts
• Experience with at least one programming language (Python preferred)
• An active AWS account for hands-on labs
• No prior AI or machine learning experience required