Overview
This course provides a comprehensive guide to building generative AI applications using Azure's suite of tools and services. Participants will explore the Azure AI Foundry portal, learn to select and deploy models from the model catalogue, and develop AI applications utilizing the Azure AI Foundry SDK. The curriculum emphasizes practical skills in prompt engineering, Retrieval-Augmented Generation (RAG), and responsible AI implementation, ensuring learners can create effective and ethical AI solutions.
Prerequisites
Participants should have:
- A foundational understanding of Azure services and cloud computing concepts
- Experience with REST APIs and JSON
- Familiarity with large language models (LLMs) and natural language processing (NLP) concepts
- Basic proficiency in programming (Python or C# preferred)
Target audience
This course is suitable for:
- AI engineers, solution architects, and software developers
- Technical professionals interested in generative AI applications
- Organisations aiming to improve service automation and internal productivity with custom AI solutions
Learning objectives
By the end of this course, learners will be able to:
- Navigate the Azure AI Foundry portal and understand its core components
- Select and deploy appropriate models from the Azure AI Foundry model catalog
- Develop AI applications using the Azure AI Foundry SDK
- Implement Retrieval-Augmented Generation (RAG) techniques with custom data
- Fine-tune language models for specific tasks
- Evaluate and optimize the performance of generative AI applications
- Apply responsible AI practices in the development and deployment of AI solutions
Course Outline
Introduction to Azure AI Foundry
- Overview of Azure AI Foundry and its capabilities
- Understanding the AI development lifecycle in Azure
- Setting up the development environment
Model selection and deployment
- Exploring the Azure AI Foundry model catalogue
- Criteria for selecting appropriate models
- Deploying models using the Azure AI Foundry portal
Developing AI applications with Azure AI Foundry SDK
- Introduction to the Azure AI Foundry SDK
- Building AI applications using the SDK
- Integrating AI capabilities into existing applications
Implementing Retrieval-Augmented Generation (RAG)
- Understanding RAG and its benefits
- Connecting to custom data sources
- Creating indexes and integrating them with generative AI models
Fine-tuning language models
- Overview of model fine-tuning processes
- Training models for specific tasks
- Evaluating fine-tuned model performance
Evaluating and optimizing AI applications
- Monitoring application performance
- Using Azure tools for evaluation
- Implementing improvements based on evaluation results
Responsible AI practices
- Understanding ethical considerations in AI development
- Implementing measures to mitigate risks
- Ensuring compliance with data privacy regulations
Exams and assessments
There are no formal exams included in this course. Learners will complete interactive labs, guided exercises, and scenario-based tasks to reinforce understanding and assess their progress.
Hands-on learning
This course includes:
- Guided labs on model deployment, application development, and RAG implementation
- Practical exercises for fine-tuning models and evaluating performance
- Simulated real-world scenarios for applying responsible AI practices
- Instructor feedback and collaborative learning activities
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QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
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When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.