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

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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

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Course Modules

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

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

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

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)

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

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

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

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

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

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

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

Manage speech by using the Speech service

implement text-to-speech
customize text-to-speech
implement speech-to-text
improve speech-to-text accuracy

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

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

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

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

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

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

Implement a knowledge store

define file projections
define object projections
define table projections
query projections

Manage a Cognitive Search solution

provision Cognitive Search
configure security for Cognitive Search
configure scalability for Cognitive Search

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

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

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