Azure AI Services For Developers

About the Seminar

This hands-on, dynamic seminar introduces participants to the world of Artificial Intelligence services in Microsoft Azure. Throughout the day, attendees will explore Azure’s intelligent services  from Cognitive Services to OpenAI and Azure Machine Learning — and learn how to integrate capabilities such as sentiment analysis, natural language processing, GPT-4, and custom AI models into real-world business solutions.

Participants will gain practical experience in developing, securing, deploying, and connecting AI components to business applications and workflows using advanced tools such as:

  • Azure Logic Apps 
  • Azure Functions
  • GitHub Actions

Target Audience

Developers, Software Engineers, Data Scientists, DevOps Engineers, and Azure-based Software Developers.

Prerequisites

Basic familiarity with Microsoft Azure.

Topics Covered

Introduction to Azure AI Services

  • Overview of Microsoft Azure and its AI capabilities

  • Mapping AI Services in Azure

  • Azure Cognitive Services

  • Azure OpenAI Service

  • Azure Machine Learning

  • Choosing between managed services and custom models

Cognitive Services API – Ready-to-Use AI Insights

  • Computer Vision, Face API, and OCR

  • Speech to Text / Text to Speech

  • Text Analytics & Language Understanding (LUIS)

  • Hands-on demo: Build a sentiment detection service from text

Azure OpenAI Service – Integrating GPT into Business Solutions

  • Introduction to Azure OpenAI and differences from OpenAI public API

  • Resource provisioning, permissions, and security

  • Working with GPT, Codex, and Embeddings models

  • Live demo: Building a GPT-4-based chatbot

Developing Custom Models with Azure Machine Learning

  • Azure ML Workspaces, Datasets, and Pipelines

  • AutoML and Responsible AI principles

  • Demo: Training an image classification model using Azure ML

Connecting AI Components to Real Applications

  • Integrating Cognitive Services, OpenAI, and Azure Functions

  • Using Azure Logic Apps and Power Automate

  • Full example: A recommendation system using GPT and sentiment analysis

Deployment and Operations in Azure AI

  • Deployment options: Web Apps, Containers, API Management

  • Monitoring with Application Insights

  • Security with Azure Key Vault and network restrictions

  • CI/CD pipelines for AI projects using GitHub Actions or Azure DevOps

Best Practices and Real-World Case Studies

  • Responsible AI principles

  • When not to use AI

  • Case study: Integrating Azure AI in a healthcare organization