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Designing and Implementing Microsoft Azure AI Solution (AI-102)

( Duration: 4 Days )

This Designing and Implementing Microsoft Azure AI Solution (AI-102) training course provides skills to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.

By attending Designing and Implementing Microsoft Azure AI Solution (AI-102) workshop, delegates will learn to:

  • Describe considerations for AI-enabled application development
  • Create, configure, deploy, and secure Azure Cognitive Services
  • Develop applications that analyze text
  • Develop speech-enabled applications
  • Create applications with natural language understanding capabilities
  • Create QnA applications
  • Create conversational solutions with bots
  • Use computer vision services to analyze images and videos
  • Create custom computer vision models
  • Develop applications that detect, analyze, and recognize faces
  • Develop applications that read and process text in images and documents
  • Create intelligent search solutions for knowledge mining

  • Familiar with C# and Python.
  • Know how to use Microsoft Azure.
  • Some experience with JSON and REST programming semantics.
  • Work experience in application and software development.

The Designing and Implementing Microsoft Azure AI Solution (AI-102) class is ideal for:

  • Software engineers
  • AI developers
  • Web developers
  • App developers
  • UI/UX developers
  • DevOps engineers
  • Agile engineers
  • IT managers
  • Project managers
  • Aspiring Artificial Intelligence Professionals
  • Professionals who are looking to clear their Designing and Implementing Microsoft Azure AI Solution certification exam

COURSE AGENDA

1

Select the appropriate Cognitive Services resource

  • Select the appropriate cognitive service for a vision solution
  • Select the appropriate cognitive service for a language analysis solution
  • Select the appropriate cognitive service for a decision support solution
  • Select the appropriate cognitive service for a speech solution
2

Plan and configure security for a Cognitive Services solution

  • Manage Cognitive Services account keys
  • Manage authentication for a resource
  • Secure Cognitive Services by using Azure Virtual Network
  • Plan for a solution that meets responsible AI principles
3

Create a Cognitive Services resource

  • Create a Cognitive Services resource
  • Configure diagnostic logging for a Cognitive Services resource
  • Manage Cognitive Services costs
  • Monitor a cognitive service
  • Implement a privacy policy in Cognitive Services
4

Plan and implement Cognitive Services containers

  • Identify when to deploy to a container
  • Containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics, Speech, Form Recognizer)
5

Analyze images by using the Computer Vision API

  • Retrieve image descriptions and tags by using the Computer Vision API
  • Identify landmarks and celebrities by using the Computer Vision API
  • Detect brands in images by using the Computer Vision API
  • Moderate content in images by using the Computer Vision API
  • Generate thumbnails by using the Computer Vision API
6

Extract text from images

  • Extract text from images by using the OCR API
  • Extract text from images or PDFs by using the Read API
  • Convert handwritten text by using Ink Recognizer
  • Extract information from forms or receipts by using the pre-built receipt model in Form Recognizer
  • Build and optimize a custom model for Form Recognizer
7

Extract facial information from images

  • Detect faces in an image by using the Face API
  • Recognize faces in an image by using the Face API
  • Configure persons and person groups
  • Analyze facial attributes by using the Face API
  • Match similar faces by using the Face API
8

Implement image classification by using the Custom Vision service

  • Label images by using the Computer Vision Portal
  • Train a custom image classification model in the Custom Vision Portal
  • Train a custom image classification model by using the SDK
  • Manage model iterations
  • Evaluate classification model metrics
  • Publish a trained iteration of a model
  • Export a model in an appropriate format for a specific target
  • Consume a classification model from a client application
  • Deploy image classification custom models to containers
9

Implement an object detection solution by using the Custom Vision service

  • Label images with bounding boxes by using the Computer Vision Portal
  • Train a custom object detection model by using the Custom Vision Portal
  • Train a custom object detection model by using the SDK
  • Manage model iterations
  • Evaluate object detection model metrics
  • Publish a trained iteration of a model
  • Consume an object detection model from a client application
  • Deploy custom object detection models to containers
10

Analyze video by using Video Indexer

  • Process a video
  • Extract insights from a video
  • Moderate content in a video
  • Customize the Brands model used by Video Indexer
  • Customize the Language model used by Video Indexer by using the Custom Speech service
  • Customize the Person model used by Video Indexer
  • Extract insights from a live stream of video data
11

Analyze text by using the Text Analytics service

  • Retrieve and process key phrases
  • Retrieve and process entity information (people, places, urls, etc.)
  • Retrieve and process sentiment
  • Detect the language used in text
12

Manage speech by using the Speech service

  • Implement text-to-speech
  • Customize text-to-speech
  • Implement speech-to-text
  • Improve speech-to-text accuracy
13

Translate language

  • Translate text by using the Translator service
  • Translate speech-to-speech by using the Speech service
  • Translate speech-to-text by using the Speech service
14

Build an initial language model by using Language Understanding Service (LUIS)

  • Create intents and entities based on a schema, and then add utterances
  • Create complex hierarchical entities
  • Use this instead of roles
  • Train and deploy a model
15

Iterate on and optimize a language model by using LUIS

  • Implement phrase lists
  • Implement a model as a feature (i.e. Prebuilt entities)
  • Manage punctuation and diacritics
  • Implement active learning
  • Monitor and correct data imbalances
  • Implement patterns
16

Manage a LUIS model

  • Manage collaborators
  • Manage versioning
  • Publish a model through the portal or in a container
  • Export a LUIS package
  • Deploy a LUIS package to a container
  • Integrate Bot Framework (LUDown) to run outside of the LUIS portal
17

Implement a Cognitive Search solution

  • Create data sources
  • Define an index
  • Create and run an indexer
  • Query an index
  • Configure an index to support autocomplete and autosuggest
  • Boost results based on relevance
  • Implement synonyms
18

Implement an enrichment pipeline

  • Attach a Cognitive Services account to a skillset
  • Select and include built-in skills for documents
  • Implement custom skills and include them in a skillset
19

Implement a knowledge store

  • Define file projections
  • Define object projections
  • Define table projections
  • Query projections
20

Manage a Cognitive Search solution

  • Provision Cognitive Search
  • Configure security for Cognitive Search
  • Configure scalability for Cognitive Search
21

Manage indexing

  • Manage re-indexing
  • Rebuild indexes
  • Schedule indexing
  • Monitor indexing
  • Implement incremental indexing
  • Manage concurrency
  • Push data to an index
  • Troubleshoot indexing for a pipeline
22

Create a knowledge base by using QnA Maker

  • Create a QnA Maker service
  • Create a knowledge base
  • Import a knowledge base
  • Train and test a knowledge base
  • Publish a knowledge base
  • Create a multi-turn conversation
  • Add alternate phrasing
  • Add chit-chat to a knowledge base
  • Export a knowledge base
  • Add active learning to a knowledge base
  • Manage collaborators
23

Design and implement conversation flow

  • Design conversation logic for a bot
  • Create and evaluate *.chat file conversations by using the Bot Framework Emulator
  • Add language generation for a response
  • Design and implement adaptive cards
24

Create a bot by using the Bot Framework SDK

  • Implement dialogs
  • Maintain state
  • Implement logging for a bot conversation
  • Implement a prompt for user input
  • Add and review bot telemetry
  • Implement a bot-to-human handoff
  • Troubleshoot a conversational bot
  • Add a custom middleware for processing user messages
  • Manage identity and authentication
  • Implement channel-specific logic
  • Publish a bot
25

Create a bot by using the Bot Framework Composer

  • Implement dialogs
  • Maintain state
  • Implement logging for a bot conversation
  • Implement prompts for user input
  • Troubleshoot a conversational bot
  • Test a bot by using the Bot Framework Emulator
  • Publish a bot
26

Integrate Cognitive Services into a bot

  • Integrate a QnA Maker service
  • Integrate a LUIS service
  • Integrate a Speech service
  • Integrate Dispatch for multiple language models
  • Manage keys in app settings file

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