AI-102T00 - Designing and Implementing a Microsoft Azure AI Solution

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The Certification & Training course will use C# or Python as the programming language.

AED 3000

Date : 23 Jul 2024

Code: ai-102t00

Duration: 4.0 days

other dates

Schedule

Virtual ILT | 23 Jul 2024 - 26 Jul 2024
Virtual ILT | 12 Aug 2024 - 15 Aug 2024
Virtual ILT | 03 Sep 2024 - 06 Sep 2024
Virtual ILT | 21 Oct 2024 - 24 Oct 2024
Virtual ILT | 11 Nov 2024 - 14 Nov 2024
Virtual ILT | 09 Dec 2024 - 12 Dec 2024

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Objectives

  • Define artificial intelligence
  • Understand AI-related terms
  • Understand considerations for AI Engineers
  • Understand considerations for responsible AI
  • Understand capabilities of Azure Machine Learning
  • Understand capabilities of Azure AI Services
  • Understand capabilities of Azure OpenAI Service
  • Understand capabilities of Azure AI Search
  • Create Azure AI services resources in an Azure subscription.
  • Identify endpoints, keys, and locations required to consume an Azure AI services resource.
  • Use a REST API and an SDK to consume Azure AI services.
  • Consider authentication for Azure AI services
  • Manage network security for Azure AI services
  • Monitor Azure AI services costs.
  • Create alerts and view metrics for Azure AI services.
  • Manage Azure AI services diagnostic logging.
  • Create containers for reuse
  • Deploy to a container and secure a container
  • Consume Azure AI services from a container
  • Provision an Azure AI Vision resource
  • Analyze an image
  • Generate a smart-cropped thumbnail
  • Create a custom Azure AI Vision classification model
  • Understand image classification
  • Understand object detection
  • Train an image classifier in Vision Studio
  • Identify options for face detection, analysis, and identification.
  • Understand considerations for face analysis.
  • Detect faces with the Computer Vision service.
  • Understand capabilities of the Face service.
  • Compare and match detected faces.
  • Implement facial recognition.
  • Read text from images using OCR
  • Use the Azure AI Vision service Image Analysis with SDKs and the REST API
  • Develop an application that can read printed and handwritten text
  • Describe Azure Video Indexer capabilities
  • Extract custom insights
  • Use Azure Video Indexer widgets and APIs
  • Detect language from text
  • Analyze text sentiment
  • Extract key phrases, entities, and linked entities
  • Understand question answering and how it compares to language understanding.
  • Create, test, publish, and consume a knowledge base.
  • Implement multi-turn conversation and active learning.
  • Create a question answering bot to interact with using natural language.
  • Provision Azure resources for Azure AI Language resource
  • Define intents, utterances, and entities
  • Use patterns to differentiate similar utterances
  • Use pre-built entity components
  • Train, test, publish, and review an Azure AI Language model
  • Understand types of classification projects
  • Build a custom text classification project
  • Tag data, train, and deploy a model
  • Submit classification tasks from your own app
  • Understand tagging entities in extraction projects
  • Understand how to build entity recognition projects
  • Provision a Translator resource
  • Understand language detection, translation, and transliteration
  • Specify translation options
  • Define custom translations
  • Provision an Azure resource for the Azure AI Speech service
  • Use the Azure AI Speech to text API to implement speech recognition
  • Use the Text to speech API to implement speech synthesis
  • Configure audio format and voices
  • Use Speech Synthesis Markup Language (SSML)
  • Provision Azure resources for speech translation.
  • Generate text translation from speech.
  • Synthesize spoken translations.
  • Create an Azure AI Search solution
  • Develop a search application
  • Implement a custom skill for Azure AI Search
  • Integrate a custom skill into an Azure AI Search skillset
  • Create a knowledge store from an Azure AI Search pipeline
  • View data in projections in a knowledge store
  • Use Azure AI Language to enrich Azure AI Search indexes.
  • Enrich an AI Search index with custom classes.
  • Improve the ranking of a document with term boosting
  • Improve the relevance of results by adding scoring profiles
  • Improve an index with analyzers and tokenized terms
  • Enhance an index to include multiple languages
  • Improve search experience by ordering results by distance from a given reference point
  • Understand how to use a custom Azure Machine Learning skillset.
  • Use Azure Machine Learning to enrich Azure AI Search indexes.
  • Use Azure Data Factory to copy data into an Azure AI Search Index
  • Use the Azure AI Search push API to add to an index from any external data source
  • Use Language Studio to enrich Azure AI Search indexes
  • Enrich an AI Search index with custom classes
  • Describe semantic ranking
  • Set up semantic ranking
  • Perform semantic ranking on an index
  • Describe vector search
  • Describe embeddings
  • Run vector search queries using the REST API
  • Describe the components of an Azure AI Document Intelligence solution.
  • Create and connect to Azure AI Document Intelligence resources in Azure.
  • Choose whether to use a prebuilt, custom, or composed model.
  • Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
  • Analyze forms by using the General Document, Read, and Layout models.
  • Analyze forms by using financial, ID, and tax prebuilt models.
  • Identify how Document intelligence's layout service, prebuilt models, and custom models can automate processes.
  • Use Document intelligence's capabilities with SDKs, REST API, and Document Intelligence Studio.
  • Develop and test custom models.
  • Describe business problems that you would use custom models and composed models to solve.
  • Train a custom model to obtain data from forms with unusual structures.
  • Create a composed model that can analyze forms in multiple formats.
  • Describe how a custom skill can enrich content passed through an Azure AI Search pipeline.
  • Build a custom skill that calls an Azure Forms Analyzer solution to obtain data from forms.
  • Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
  • Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio's playgrounds.
  • Generate completions to prompts and begin to manage model parameters.
  • Integrate Azure OpenAI into your application
  • Differentiate between different endpoints available to your application
  • Generate completions to prompts using the REST API and language specific SDKs
  • Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models' performance.
  • Know how to design and optimize prompts to better utilize AI models.
  • Include clear instructions, request output composition, and use contextual content to improve the quality of the model's responses.
  • Use natural language prompts to write code
  • Build unit tests and understand complex code with AI models
  • Generate comments and documentation for existing code
  • Describe the capabilities of DALL-E in the Azure openAI service
  • Use the DALL-E playground in Azure OpenAI Studio
  • Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps
  • Describe the capabilities of Azure OpenAI on your data
  • Configure Azure OpenAI to use your own data
  • Use Azure OpenAI API to generate responses based on your own data
  • 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

Content

1. Get started with Azure AI Services

Azure AI Services is a collection of services that are building blocks of AI functionality you can integrate into your applications. In this learning path, you'll learn how to provision, secure, monitor, and deploy Azure AI Services resources and use them to build intelligent solutions.

Click here to know more

2. Create computer vision solutions with Azure AI Vision

Computer vision is an area of artificial intelligence that deals with visual perception. Azure AI Vision includes multiple services that support common computer vision scenarios.

Click here to know more

3. Develop natural language processing solutions with Azure AI Services

Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications.

Click here to know more

4. Implement knowledge mining with Azure AI Search

Do you have information locked up in structured and unstructured data sources? Using Azure AI Search, you can extract key insights from this data, and enable applications to search and analyze them.

Click here to know more

5. Develop solutions with Azure AI Document Intelligence

In this learning path, discover how Azure AI Document Intelligence solutions can enable you to capture data from typed or hand-written forms. Learn how to build a solution for your custom form types and integrate that solution into an Azure Cognitive Search pipeline.

Click here to know more

6. Develop Generative AI solutions with Azure OpenAI Service

Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio.

Click here to know more

Audience

Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.

Prerequisites

Please review the prerequisites listed for each module in the course content and click on the provided links for more information.

Certification

product-certification

Skills measured

  • Plan and manage an Azure AI solution
  • Implement decision support solutions
  • Implement computer vision solutions
  • Implement natural language processing solutions
  • Implement knowledge mining and document intelligence solutions
  • Implement generative AI solutions

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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