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Master Generative AI on AWS Cloud : In-Depth Training
4.2
4.2/5
Price :
₹2,25,000
Category :
Management
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.
- Time Duration : 1 Day
- Study Lecture : 9 Lectures
- Skill Level : Advance Course
- Certificate : Yes
- Training Delivery Mode : Online - Live Instructor Led training
OVERVIEW :
COURSE DESCRIPTION
COURSE SUMMARY
COURSE OBJECTIVE
COURSE DESCRIPTION
Dive deep into the world of Generative AI with a specific focus on AWS Cloud. This course will guide participants through the foundational concepts of Generative AI, introducing them to AWS’s unique tools like DeepComposer and SageMaker. With hands-on labs, participants will experience real-world applications, model training, and deployment in the AWS environment.
COURSE SUMMARY
Generative AI on AWS Cloud offers a comprehensive exploration of generative artificial intelligence within the AWS ecosystem. Starting with foundational knowledge, the course progressively introduces AWS-specific tools, ensuring participants gain hands-on experience. With real-world applications, interactive labs, and expert guidance, participants will leave with a solid grasp of Generative AI implementation on AWS.
COURSE OBJECTIVE
By the end of this course, participants will be able to:
- Understand the principles and applications of Generative AI.
- Navigate and utilize AWS tools and services tailored for Generative AI, including DeepComposer and SageMaker.
- Train, fine-tune, and deploy generative models on AWS.
- Address common challenges in Generative AI and apply best practices for optimization.
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.
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.
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.
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