• Registration and Welcome Breakfast

• Introduction to Generative AI

  – Definition and significance of Generative AI.

  – Overview of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other generative models.

  – Applications and potential of Generative AI.

• Introduction to AWS Cloud

  – Overview of Amazon Web Services (AWS).

  – Highlight of AWS’s AI & Machine Learning services.

• Hands-on Lab 1: Setting up AWS for Generative AI Workloads

  – Creating an AWS account and setting up IAM roles.

  – Introduction to Amazon SageMaker and its relevance to AI/ML.

  – Initial configuration for generative AI workloads.

• Morning Break

• Dive into AWS Generative AI Tools

  – DeepComposer: Generative AI for music.

  – DeepRacer: Reinforcement learning models.

  – Overview of SageMaker’s capabilities for custom generative models.

• Hands-on Lab 2: Exploring Deep Composer

  – Setting up DeepComposer.

  – Training a generative model for music generation.

  – Evaluating and fine-tuning the model’s outputs.

Lunch Break

Advanced Generative AI with SageMaker

– Benefits of using SageMaker for generative AI tasks.

– Integrating other AWS services (like S3) with SageMaker for data management.

– Custom generative model training and deployment.

Hands-on Lab 3: Training a GAN with SageMaker

– Setting up the SageMaker environment.

– Preparing datasets and training a GAN model.

– Visualizing and interpreting generated samples.

Afternoon Break

Challenges and Solutions in Generative AI on AWS

– Addressing common issues: mode collapse, training instability, etc.

– AWS tools and resources for troubleshooting.

– Best practices for model optimization and performance.

Hands-on Lab 4: Fine-tuning and Deployment

– Advanced techniques for improving generative model outputs.

– Deploying the trained model for real-time generation tasks.

– Scaling and managing generative AI solutions on AWS.

Q&A, Feedback, and Closing Remarks

End of Training

This course aims to provide a comprehensive insight into Generative AI on AWS. Ensure to adjust pacing based on the participants’ prior knowledge and always incorporate feedback after each hands-on lab to gauge understanding and make necessary adjustments.