AI-3016 Develop Generative AI apps in Azure
Guaranteed to Run
Price
$695.00
Duration
1 Day
Delivery Methods
Virtual Instructor Led Private Group
Delivery
Virtual
ESTDescription
Objectives
Prerequisites
Content
Course Description
Generative Artificial Intelligence (AI) is becoming more accessible through easy-to-use platforms like Azure AI Studio. Learn how to build generative AI applications like custom copilots that use language models and prompt flow to provide value to your users.
Course Objectives
- Plan & Prepare – Assess risks and plan development of generative AI solutions on Azure.
- Azure AI Foundry – Navigate and manage Azure AI Foundry and Azure AI Studio features.
- Model Deployment – Select and deploy models from the catalog for project needs.
- Language Model Apps – Use prompt flow, integrate data with RAG, and enhance model capabilities.
- Fine-Tuning – Adapt language models for specific tasks and requirements.
- Responsible AI – Apply responsible practices in solution design and deployment.
- Performance Evaluation – Measure and optimize application performance.
Who Should Attend?
- Data Scientists & AI Engineers – build and deploy generative AI apps and custom copilots in Azure
- Software Developers – integrate large language models into applications (programming experience recommended)
- AI & Solutions Architects – design and oversee Azure AI Foundry implementations
- MLOps & Cloud Engineers – manage ML model lifecycles and infrastructure with generative AI workflows
- IT Professionals – expand skills in deploying generative AI solutions on Azure
Course Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure.
Course Content
Module 1: Introduction to Azure AI Studio
- What is Azure AI Studio?
- How it works and when to use it
Module 2: Model Catalog and Deployment
- Explore language models in the catalog
- Deploy models to endpoints
- Optimize model performance
Module 3: Prompt Flow Development
- LLM app development lifecycle
- Core components and flow types
- Connections, runtimes, variants, and monitoring
Module 4: RAG-based Copilot Solutions
- Grounding language models with your own data
- Making data searchable
- Building copilots with prompt flow
Module 5: Fine-Tuning and Integration
- When and how to fine-tune a model
- Preparing data for chat completion fine-tuning
- Integrating fine-tuned models with copilots
Module 6: Performance Evaluation
- Evaluate model and copilot performance
- Manual and automated assessments
Module 7: Responsible Generative AI
- Planning responsible AI solutions
- Identifying, measuring, and mitigating potential harms
- Operating responsibly in Azure AI Studio
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