Vendor: ITIL

Tehnologije: IT Service Management

Ovaj trening nije osmišljen na način da ćete njegovim pohađanjem postati podatkovni znanstvenici ili programeri softvera, već kako bi izgradio svijest o uobičajenim radnim opterećenjima umjetne inteligencije i sposobnost identificiranja Azure usluga koje ih podržavaju.

Osnovne informacije

  1. Opisati umjetnu inteligenciju.
  2. Opisati osnovne principe strojnog učenja (engl. machine learning) na Azureu.
  3. Opisati značajke računalnog vida (engl. computer vision) na Azureu.
  4. Opisati značajke Natural Language Processing (NLP) na Azureu.
  5. Opisati generativnu umjetnu inteligenciju.
  6. Opisati Open AI značajke i usluge.

Svima zainteresiranima za usvajanje znanja i vještina o rješenjima koje umjetna inteligencija (engl. Artificial Intelligence, AI) omogućava i uslugama na Microsoft Azureu pomoću kojih je ta rješenja moguće stvoriti. AI inženjerima, podatkovnim znanstvenicima, programerima, arhitektima (engl. AI Engineer, Data Scientist, Developer, Solutions Architect).

Općenita znanja iz IT područja. Neki od pojmova obuhvaćenih treningom zahtijevaju osnovno razumijevanje matematike, poput sposobnosti tumačenja grafova. Trening uključuje praktične aktivnosti koje uključuju rad s podacima i kodom, pa će poznavanje temeljnih programskih principa biti korisno. Znanje u Azureu i iskustvo u radu s Azureom nije potrebno.

Certifikacijski ispit

Exam AI-900: Microsoft Azure AI Fundamentals

Certifikat

Microsoft Certified: Azure AI Fundamentals

Moduli koji će se izvoditi

With AI, we can build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone. After completing this module, students will be able to:

Identify kinds of solutions AI can make possible and considerations for responsible AI practices

Machine learning is the basis for most modern artificial intelligence solutions. A familiarity with the core concepts on which machine learning is based is an important foundation for understanding AI. After completing this module, students will be able to:

Describe core concepts of machine learning
Identify different types of machine learning
Describe considerations for training and evaluating machine learning models
Describe core concepts of deep learning
Use automated machine learning in Azure Machine Learning service

In this module, you learn the fundamentals of how Azure AI services can be used to build applications. After completing this module, students will be able to:

Understand applications Azure AI services can be used to build
Understand how to access Azure AI services in the Azure portal
Understand how to use Azure AI services keys and endpoint for authentication
Create and use an Azure AI services resource in a Content Safety Studio setting

Azure AI Vision service enables software engineers to create intelligent solutions that extract information from images; a common task in many artificial intelligence (AI) scenarios. After completing this module, students will be able to:

Use the Azure AI Vision service to analyze images

Face detection, analysis, and recognition are important capabilities for artificial intelligence (AI) solutions. Azure AI Face service in Azure makes it easy integrate these capabilities into your applications. After completing this module, students will be able to:

Use Azure AI Face service to detect and analyze faces in images

Optical character recognition (OCR) enables artificial intelligence (AI) systems to read text in images, enabling applications to extract information from photographs, scanned documents, and other sources of digitized text. After completing this module, students will be able to: Read text in images with Azure AI Vision Module 7: Fundamentals of Text Analysis with the Language Service Explore Azure AI Language’s natural language processing (NLP) features, which include sentiment analysis, key phrase extraction, named entity recognition, and language detection. After completing this module, students will be able to:

Use Azure AI Language for text analysis

Explore Azure AI Language’s natural language processing (NLP) features, which include sentiment analysis, key phrase extraction, named entity recognition, and language detection. After completing this module, students will be able to:

Use Azure AI Language for text analysis

Create a custom question answering knowledge base with Azure AI Language and create a bot with Azure AI Bot Service that answers user questions. After completing this module, students will be able to:

Understand how to use Azure AI Language and Azure AI Bot Service to create a bot

In this module, we introduce you to conversational language understanding, and show how to create applications that understand language with Azure AI Language. After completing this module, students will be able to:

Learn what conversational language understanding is
Learn about key features, such as intents and utterances
Build and publish a natural-language machine-learning model

In this module you will learn how to recognize and synthesize speech by using Azure AI Speech. After completing this module, students will be able to:

Learn about speech recognition and synthesis
Learn how to use Azure AI Speech

Document processing is a common task in many business scenarios. Organizations can use Azure AI Document Intelligence to automate data extraction across document types, such as receipts, invoices, and more. After completing this module, students will be able to:

Use the prebuilt receipt processing capabilities of Azure AI Document Intelligence

In this module you will learn how to use Azure Cognitive Search to make your data searchable. After completing this module, students will be able to:

Explore Azure Cognitive Search
Create an Azure Cognitive Search index
Import data to the index
Query the Azure Cognitive Search index

In this module you’ll explore the way in which large language models (LLMs) enable AI applications and services to generate original content based on natural language input. You’ll also learn how generative AI enables the creation of AI-powered copilots that can assist humans in creative tasks. After completing this module, students will be able to:

Understand generative AI’s place in the development of artificial intelligence
Understand large language models and their role in intelligent applications
Describe how Azure OpenAI supports intelligent application creation
Describe examples of copilots and good prompts

Get to know the connection between artificial intelligence (AI), Responsible AI, and text, code, and image generation. Understand how you can use Azure OpenAI to build solutions against AI models within Azure. After completing this module, students will be able to:

Describe Azure OpenAI workloads and access the Azure OpenAI Service
Understand generative AI models
Understand Azure OpenAI’s language, code, and image capabilities
Understand Azure OpenAI’s responsible AI practices and limited access policies

Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation. After completing this module, students will be able to:

Describe an overall process for responsible generative AI solution development
Identify and prioritize potential harms relevant to a generative AI solution
Measure the presence of harms in a generative AI solution
Mitigate harms in a generative AI solution
Prepare to deploy and operate a generative AI solution responsibly

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