Designing and Implementing a Microsoft Azure AI Solution – Intensive Training («AI102»)

• Planning and managing an Azure AI solution
• Implementing decision support solutions
• Implementing computer vision solutions
• Implementing natural language processing solutions
• Implementing knowledge mining and document intelligence solutions
• Implementing generative AI solutions

• As an aspiring Azure AI Engineer, you should understand core concepts and principles of AI development, and the capabilities of Azure services used in AI solutions.

• Azure AI services enable developers to easily add AI capabilities into their applications. Learn how to create and consume these services.

• Securing Azure AI services can help prevent data loss and privacy violations for user data that may be a part of the solution.

• Azure AI services enable you to integrate artificial intelligence into your applications and services. It's important to be able to monitor Azure AI Services in order to track utilization, determine trends, and detect and troubleshoot issues.

• Learn about Container support in Azure AI services allowing the use of APIs available in Azure and enable flexibility in where to deploy and host the services with Docker containers.

• With the Azure AI Vision service, you can use pre-trained models to analyze images and extract insights and information from them.

• Image classification is used to determine the main subject of an image. You can use the Azure AI Custom Vision services to train a model that classifies images based on your own categorizations.

• The ability for applications to detect human faces, analyze facial features and emotions, and identify individuals is a key artificial intelligence capability.

• Azure's Azure AI Vision service uses algorithms to process images and return information. This module teaches you how to use the Read API for optical character recognition (OCR).

• Azure Video Indexer is a service to extract insights from video, including face identification, text recognition, object labels, scene segmentations, and more.

• The Azure AI Language service enables you to create intelligent apps and services that extract semantic information from text.

• The question answering capability of the Azure AI Language service makes it easy to build applications in which users ask questions using natural language and receive appropriate answers.

• The Azure AI Language conversational language understanding service (CLU) enables you to train a model that apps can use to extract meaning from natural language.

• The Azure AI Language service enables processing of natural language to use in your own app. Learn how to build a custom text classification project.

• Build a custom entity recognition solution to extract entities from unstructured documents

• The Translator service enables you to create intelligent apps and services that can translate text between languages.

• The Azure AI Speech service enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.

• Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language, and returning translations of the transcription in one or more other languages.

• Unlock the hidden insights in your data with Azure Cognitive Search.

• Use the power of artificial intelligence to enrich your data and find new insights.

• Persist the output from an Azure Cognitive Search enrichment pipeline for independent analysis or downstream processing.

• Learn how to use Azure AI Document Intelligence to build solutions that analyze forms and output data for storage or further processing.

• Learn what data you can analyze by choosing prebuilt Azure AI Document Intelligence models and how to deploy these models in a Document Intelligence solution.

• Azure Document Intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document Intelligence Azure AI service.

• This module provides engineers with the skills to begin building an Azure OpenAI Service solution.

• This module provides engineers with the skills to begin building apps that integrate with the Azure OpenAI Service.

• Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.

• This module shows engineers how to use the Azure OpenAI Service to generate and improve code.

• The Azure OpenAI service includes the DALL-E model, which you can use to generate original images based on natural language prompts.

• Azure OpenAI on your data allows developers to use supported AI chat models that can reference specific sources of data to ground the response.

• Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.

Before attending this course, students must have:

• Knowledge of Microsoft Azure and ability to navigate the Azure portal
• Knowledge of either C# or Python
• Familiarity with JSON and REST programming semantics

To gain C# or Python skills, complete the free Take your first steps with C# or Take your first steps with Python learning path before attending the course.

If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.

This intensive training prepares you for:

• Exam: «AI-102: Designing and Implementing an Azure AI Solution» for the
• Certification: «Microsoft Certified Azure AI Engineer Associate»

• Microsoft Certified: Azure AI Engineer Associate