Cloud Enabled SG

Master Generative AI on AWS Cloud : In-Depth Training

4.2
4.2/5
Price :

650 SGD

Category :
Management
Consultant 1
Anil Bidari

Chief Consultant

Anil Bidari is a versatile trainer and consultant specializing in GitLab, AWS, Azure, Google, DevOps, Jenkins, Kubernetes, Ansible, Docker, Agile, and Machine Learning. His expertise drives successful technology adoption and implementation, benefiting organizations and individuals alike.
AWS Cloud–Training 1
OVERVIEW :
Of course! Here's a one-day training outline focused on Generative AI solutions using AWS (Amazon Web Services) cloud

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.

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.

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.

 

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.

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.

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.

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.

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.

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.

Let's Enroll Our Course !!

Cloud Enabled Pvt Ltd is your trusted partner in advancing your skills. We offer comprehensive training in Cloud Computing, DevOps, and Machine Learning, designed to propel your career.

×