• Cloud Architects and DevOps Engineers looking to integrate AI into AWS-based applications. Data Scientists and Machine Learning Engineers interested in leveraging AWS tools for Generative AI use cases. Software Developers looking to automate workflows and improve user experiences with AI capabilities.

• Familiarity with AWS Cloud Services (EC2, S3, Lambda, etc.), Basic understanding of machine learning concepts and AI models, Experience with AWS Management Console or equivalent tools, No prior experience with Generative AI is required, but knowledge of basic AI concepts is helpful.

Course Outline:

Session 1: Introduction to Generative AI & AWS

• Overview of Generative AI
  • What is Generative AI? (e.g., GPT, BERT, DALL·E, Codex)
  • Key use cases for Generative AI in the cloud: Content creation, text generation, image generation, and automated workflows.
  • Benefits of using AWS for Generative AI applications: Scalability, flexibility, security, and cost efficiency.
• Overview of AWS AI/ML Services
  • Introduction to Amazon SageMaker, AWS Lambda, AWS AI Services (Polly, Lex, Rekognition, Comprehend)
  • Using AWS infrastructure to run and scale Generative AI models
  • AWS tools for training and deploying AI models

Session 2: Implementing Generative AI for Content Generation

• Using Amazon Polly for Text-to-Speech
  • Overview of Amazon Polly and its capabilities
  • Creating natural-sounding speech from text with Amazon Polly
  • Hands-on demo: Implementing Polly for creating speech-based content from user input (e.g., news articles, product descriptions)
• Using AWS Lambda for AI-Driven Automation
  • Introduction to AWS Lambda and serverless computing
  • Automating content generation workflows with Lambda (e.g., using GPT-3 to generate content on demand)
  • Hands-on demo: Using Lambda to trigger Generative AI models for automated content generation

Session 3: AI for Text, Image, and Voice Applications

• AI for Text Generation and Understanding with Amazon Comprehend and GPT Models (45 mins)
  • Using Amazon Comprehend for sentiment analysis, entity recognition, and text classification
  • Integrating Generative AI models (e.g., GPT-3) for text generation and summarization tasks
  • Hands-on demo: Using Comprehend to analyze customer feedback and GPT to generate summarized reports or responses
• AI for Image Generation and Recognition with Amazon Rekognition (45 mins)
  • Introduction to Amazon Rekognition for image and video analysis
  • Generating images with Generative Adversarial Networks (GANs) in the AWS ecosystem (via SageMaker)
  • Hands-on demo: Using Rekognition for object detection and GANs for image creation in a cloud-based application

Session 4: Building Conversational AI Solutions with AWS

• Amazon Lex: Building Conversational Agents
  • Introduction to Amazon Lex for building conversational bots and virtual assistants
  • Leveraging Generative AI models (like GPT) to enhance conversation quality and context understanding
  • Hands-on demo: Building a simple AI chatbot using Lex for customer service automation
• Amazon Polly and Lex for Advanced Voice Interfaces
  • Combining Amazon Polly and Amazon Lex for multi-modal voice interactions (e.g., voice-based apps, smart assistants)
  • Integrating Generative AI for enhanced user experiences (context-aware interactions)
  • Hands-on demo: Building a voice-enabled virtual assistant that generates personalized responses using Lex and Polly

Session 5: Scaling Generative AI Models in AWS

• Amazon SageMaker for Model Training and Deployment
  • Using Amazon SageMaker for training, tuning, and deploying custom Generative AI models
  • Running pre-trained AI models (e.g., GPT, T5, BERT) on SageMaker to generate content or perform tasks
  • Hands-on demo: Training a custom text generation model in SageMaker and deploying it as an endpoint
• Optimizing Costs and Performance for Generative AI on AWS
  • Best practices for cost management when using AWS for Generative AI workloads
  • Efficiently scaling models for production using SageMaker, EC2, and Lambda
  • Monitoring performance with Amazon CloudWatch

Session 6: Real-World Use Cases and Future Trends

• Real-World Use Cases of Generative AI in AWS
  • Case studies of AI-powered applications in industries such as e-commerce, customer service, healthcare, and media (e.g., chatbots, content creation, voice assistants)
  • Demonstrating the ROI of Generative AI in cloud environments
• Future Trends in Generative AI and Cloud Computing
  • Upcoming AWS features for AI/ML (e.g., AI model marketplaces, improved inference capabilities)
  • The evolution of Generative AI and its role in cloud-native applications
  • Best practices for staying up-to-date with AWS AI/ML advancements