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Master Generative AI on Microsoft Azure: Comprehensive In-Depth Training
4.2
4.2/5
Price :
650 SGD
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
Unveil the transformative capabilities of Generative AI within the Microsoft Azure ecosystem in this comprehensive training course. Participants will embark on a hands-on journey, exploring foundational generative models, Azure-specific AI tools, and real-world applications. With a blend of expert-led lectures and practical labs, learners will gain unmatched expertise in Azure-based Generative AI solutions.
COURSE SUMMARY
The “Master Generative AI on Microsoft Azure” training provides an immersive experience in the world of Generative AI. Tailored specifically for the Azure platform, participants will delve deep into generative models, leveraging Azure’s powerful tools and services. From understanding the nuances of GANs and VAEs to deploying scalable AI solutions with Azure Kubernetes Service, this course promises both depth and breadth. Expert guidance, combined with hands-on labs, ensures participants leave with actionable skills and a deep understanding of Generative AI’s potential on Azure.
COURSE OBJECTIVE
By the end of this course, participants will be able to:
- Understand the foundational concepts and significance of Generative AI.
- Navigate and leverage Microsoft Azure’s AI & Machine Learning services tailored for Generative AI.
- Build, train, and deploy generative models using Azure Machine Learning Studio and Azure Databricks.
- Address common challenges in Generative AI and implement best practices for model optimization.
- Integrate various Azure services to enhance and scale Generative AI solutions for real-world applications.
Of course! Here's a one-day training outline focused on Generative AI solutions using Microsoft Azure Cloud
Registration and Welcome Breakfast
Introduction to Generative AI
- Definition and significance of Generative AI.
- An overview of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other generative models.
- Potential applications and impact of Generative AI.
Introduction to Microsoft Azure Cloud
- Overview of Microsoft Azure.
- Highlight of Azure's AI & Machine Learning services.
Hands-on Lab 1: Setting up Azure for Generative AI Workloads
- Initiating an Azure account and setting up resource groups.
- Introduction to Azure Machine Learning service and its relevance to AI/ML.
- Configurations tailored for generative AI workloads.
Morning Break
Dive into Azure Generative AI Tools
- Azure Machine Learning Studio: Model training and deployment.
- Azure Databricks: Distributed data analytics and ML training.
- Azure Cognitive Services: Pre-trained AI services.
Hands-on Lab 2: Building Generative Models with Azure ML Studio
- Setting up the Azure ML environment.
- Training a GAN model for data generation.
- Visualizing and interpreting generated data.
Lunch Break
Advanced Generative AI Techniques in Azure
- Leveraging Azure Databricks for large-scale generative model training.
- Integration of Azure Blob Storage for managing generated data.
- Custom generative model deployment with Azure Kubernetes Service.
Hands-on Lab 3: Distributed Generative AI Training with Azure Databricks
- Setting up a Databricks workspace.
- Parallel training of generative models.
- Optimizing and fine-tuning using distributed resources.
Afternoon Break
Challenges and Solutions in Generative AI on Azure
- Addressing issues like mode collapse, overfitting, etc.
- Azure tools and resources for troubleshooting generative AI challenges.
- Best practices for scalable and efficient generative AI solutions.
Hands-on Lab 4: Deployment and Scaling with Azure Kubernetes Service
- Packaging generative AI models for deployment.
- Setting up Azure Kubernetes clusters.
- Scaling and monitoring generative AI applications.
Q&A, Feedback, and Closing Remarks
End of Training
This course offers a holistic approach to Generative AI within the Microsoft Azure ecosystem. Adjust the pacing and depth based on the participants' prior knowledge, and always incorporate feedback after each hands-on lab to ensure optimal understanding.
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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