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


AI-102 : Designing and Implementing a Microsoft Azure AI Solution Certification Training Course Overview

Enroll for 4-day Designing and Implementing a Microsoft Azure AI Solution AI -102 certification training course from Koenig Solutions accredited by Microsoft. This training course is recommended for azure AI Engineers to learn how to use cognitive Services, Machine Learning, and Knowledge Mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, bots, and agents.

Through a blend of hands-on labs and interactive lectures, you will accomplish the following technical tasks: analyze solution requirements, design solutions, integrate AI models into solutions and deploy and manage solutions.

Target Audience:

  • Data scientists.
  • Data engineers.
  • IoT specialists.
  • Software developers.

Learning Objectives:

After completing this course, you will be able to:

  • Plan and manage an Azure Cognitive Services solution.
  • Implement Computer Vision solutions.
  • Implement natural language processing solutions.
  • Implement knowledge mining solutions.
  • Implement conversational AI solutions.

 

This course prepares you for Exam AI-102. Test your current knowledge Qubits42

AI-102: Designing and Implementing a Microsoft Azure AI Solution (Duration : 32 Hours) Download Course Contents

Live Virtual Classroom
Group Training 1500

21 - 24 Jun
09:00 AM - 05:00 PM CST
(8 Hours/Day)

05 - 08 Jul
09:00 AM - 05:00 PM CST
(8 Hours/Day)

02 - 05 Aug
09:00 AM - 05:00 PM CST
(8 Hours/Day)

GTR=Guaranteed to Run
1-on-1 Training (GTR) 1700
4 Hours
8 Hours
Week Days
Week End


Start Time : At any time

12 AM
12 PM

Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request

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Course Modules

  • 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
  • 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
  • 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
  • identify when to deploy to a container
  • containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics, Speech, Form Recognizer)
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • retrieve and process key phrases
  • retrieve and process entity information (people, places, urls, etc.)
  • retrieve and process sentiment
  • detect the language used in text
  • implement text-to-speech
  • customize text-to-speech
  • implement speech-to-text
  • improve speech-to-text accuracy
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • define file projections
  • define object projections
  • define table projections
  • query projections
  • provision Cognitive Search
  • configure security for Cognitive Search
  • configure scalability for Cognitive Search
  • manage re-indexing
  • rebuild indexes
  • schedule indexing
  • monitor indexing
  • implement incremental indexing
  • manage concurrency
  • push data to an index
  • troubleshoot indexing for a pipeline
  • 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
  • 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
  • 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
  • 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
  • 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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Course Prerequisites

Candidates for this exam should be proficient in C#, Python, or JavaScript.