Course Description

This course is taught in online mode . The approximate duration of the course is 115 hours That distributes between content and collaboration tools. Upon completion, the student will receive an accrediting diploma.

Training is done through our Virtual Campus , with this modality you will have all the didactic content on the course platform and it will be accessible, from the start day of the course, 24 hours a day, every day of the week. Access to the Campus will be available for 10 weeks.

The student will also have participation forums , as well as a continuous tutoring .

About this course

Learn how to implement Azure OpenAI technologies to develop your Artificial Intelligence program. Our Azure OpenAI technical course covers how to use the Azure environment to set up the environment for advanced AI. Advance your career and take control of your AI program with the Azure OpenAI technical course.

Addressed to

IT directors, ICT professionals, those responsible for innovation and development in the organization, Managers from different departments who want to know how Artificial Intelligence with OpenAI and Azure can address their new challenges of technological expansion, innovation professionals, employees and everyone those who wish to learn about one of the main disruptive technologies today.

A practical course to learn how to use the Azure artificial general intelligence service

Addressed to

This course is aimed at professionals, students and artificial intelligence enthusiasts who want to learn about and use the Azure OpenAI service, a cloud computing platform that offers access to state-of-the-art artificial general intelligence (AGI) models, capable of perform various cognitive tasks from natural text input.

Requirements

To take this course, you need to have a basic level of knowledge about artificial intelligence, as well as programming knowledge in one of the languages ​​supported by the Azure OpenAI SDK (Python, Java, C#, Node.js or Ruby). Additionally, you need an Azure account and an access key to the Azure OpenAI service, which can be obtained free of charge for a limited period.

Course objectives

Understand the basics of text generation with pre-trained language models and how they can be tailored to specific domains.

Learn how to use custom Azure OpenAI to create, train, and manage GPT-4 tuned models.

Explore the creative possibilities and ethical limitations of text generation with GPT-4.

Develop practical skills to implement and consume GPT-4 models from different platforms and programming languages.

Apply the knowledge acquired in a final project that consists of generating texts on a topic chosen by the student.

Explore the creative possibilities and ethical limitations of text generation with GPT-4.

Develop practical skills to implement and consume GPT-4 models from different platforms and programming languages.

Apply the knowledge acquired in a final project that consists of generating texts on a topic chosen by the student.

Unit 1: Introduction to Azure OpenAI Service

• Azure OpenAI fundamentals: what it is, how it works and what benefits it offers.
• Main concepts: Tokens, resources, implementations, models and learning in context.
• Access to Azure OpenAI: how to get an access key and how to use the Azure portal.
• Types of models in Azure OpenAI: what they are, how they are classified and what characteristics they have.
• Deploying IAG Models - How to create and configure a deployment of an Azure OpenAI model.
• Using messages to get model completions: How to send and receive messages to an implementation to get results from a model.

Unit 2: Using Azure OpenAI Studio

• GPT-3, Davinci, Curie, Babbage and Ada models: what they are, how they differ and what applications they have.
• Codex and Cushman models: what they are, how they relate to GPT-3, and what uses they have in software development and text generation.
• Model adjustment: what it is, how it is done and what benefits it has to customize a model according to the user's needs.
• How to manage content filtering: what it is, how to activate it, and how to configure it to avoid inappropriate or sensitive results.
• Creating a resource: How to create an Azure OpenAI resource and assign it a model, plan, and region.
• Managing insertions: what they are, how they are used, and how they are customized to add additional information or modify the behavior of a model.
• Completions: what they are, how they are obtained and how the results of a model are interpreted.
• Using code: How to use the Azure OpenAI SDK to interact with the service from different programming languages.
• Using Azure OpenAI Studio: How to use the Azure OpenAI web tool to create, test, and share IAG projects.
• Practice: Integrate Azure OpenAI into your app

Unit 3: Identity Management

• REST API: what it is, how it is used and what advantages it has to integrate the Azure OpenAI service with other applications or services.
• Data encryption: what it is, how it is applied and what guarantees it offers to protect data privacy and security.
• Version control: what it is, how it is done and what benefits it has for maintaining a history of changes made to an IAG project.
• REST API Swagger: What it is, how to use it, and how it's useful for documenting and testing the Azure OpenAI REST API.

Unit 4: Managing an application with Azure OpenAI

• Understand messaging engineering
• Write more effective messages
• Connection management to improve accuracy
• Using messaging engineering in your application

Unit 5: Generate code and images with Azure OpenAI Service

• Code construction from natural language
• Using GitHub copilot
• Complete code and help in the development process
• Bug fixes and code improvements
• DALL-E integration in Azure OpenAI Studio
• Using the Azure OpenAI REST API to consume DALL-E models
• Image generation

Laboratories

• A GPT-4 practical application project will be carried out with custom Azure OpenAI, in which a fine-tuned model will be created to generate texts on a specific topic, content filtering and embeds will be configured, a deployment will be created, and completions will be obtained from code and from Azure OpenAI Studio.