Is Azure AI a good career?

Azure AI engineer role involves a lot of promising potential for taking up new and challenging tasks related to AI. IT professionals could try out many new skills and activities in the role of AI engineers, thereby catering to the demands of the existing job market.

What is azure AI engineer?

Azure AI engineers build, manage, and deploy AI solutions that leverage Azure Cognitive Services and Azure Applied AI services.

Can I get a job with Azure certification?

Depending on your role, earning an Azure certification can help you access more job opportunities that may require additional credentials. When you obtain a new Microsoft Azure certification and gain more working experience, you may be able to leverage your expertise for a higher salary.

How much does an AI engineer make?

According to Datamation, the average salary for an artificial intelligence programmer is between $100,000 and $150,000. AI engineers, on the other hand, earn an average of $171,715 with the top earners making more than $250,000.

How many hours will it take to complete AI-102T00 course?

We have approximately 20 hours of course which include 15 hours of labs and quizzes, and 5hours of video lessons.

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

Instructor-led course to gain the skills needed to become a Microsoft Certified AI Engineer

What you will learn

Azure Cognitive Services
How Natural Language Processing Works
Build Speech-Enabled Applications
Recognize and Synthesize Speech
Create Language Understanding Solutions
Build a QnA Solution
Azure Bot Service
Conversational AI and Azure Bot Service
Develop Computer Vision Solutions
Analyze Videos and Classify Images
Able to Detect, Analyze, and Recognize Faces
Read Text in Images and Documents
Able to Create Knowledge Mining Solutions

Requirements

Students taking this course need internet speed of at least 10 Mbps
At least 2 monitors are recommended with 1920 x1080 resolution
Experience with Visual Studio Code
Experience with Python
Familiarity with REST programming principles and JSON
Expertise in Azure Cognitive Services, Azure Bot Service, and Azure Cognitive Search

Description

This Microsoft Azure Exam AI-102 Course teaches software developers how to design, deploy and manage AI-enhanced applications. Therefore, that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. During this course, you will learn the theory of developing AI applications using AI programming languages like Python, JSON, C++, and JavaScript and gain valuable industry knowledge through practical lab sessions designed by our highly experienced instructors.

During the course, the students will participate in all phases of AI solutions development, from requirements definition to design, deployment, maintenance, and monitoring. As a result, the knowledge of these features will allow them to collaborate with solution architects, data scientists, IoT specialists, and AI developers to effectively translate their vision into building complete end-to-end AI solutions.

Lastly, this course will equip you with the necessary skills to pass the AI-102 exam. In addition, you will earn a Microsoft Certified: Azure AI Engineer Associate certification badge that will help you on your way to becoming a successful Azure AI Engineer and get the job you always dreamed of. Even if you don't plan to take the exam, this course and its hands-on labs will help you on your way to becoming an Azure AI Engineer.

The course overall consists of 12 modules, starting with an introduction to Artificial Intelligence and Azure, followed by other modules. Each module contains three main sections including Lessons, Labs, and Quizzes. Besides, the course also contains practical demonstrations to help you perform the assigned Labs and quizzes to assess your knowledge. If you want to learn more about this course, feel free to contact us anytime.

Prominent Features

Instructor-Led Course
Live Training Option
Flexible Deadlines
Self-Paced Learning
Shareable Certificate
Skillset Building to Get Job Ready
Scenario Based Labs
Earn a Career Credential
Licenses for Lab Completion
Enhanced  Course Material
Helpdesk Support
Guidelines to Overcome Professional Challenges
Command Over Professional Expertise
Prepared Virtual Machines for Labs
Business Plan Option

Proprietary Course Enhancements

While all official preparatory information will be covered, we have enhanced this course with additional exercises and content. Instructor Brandon reserves the right to enrich the standard Microsoft course materials to add value, prepare students for real-world scenarios, and keep them up-to-date with the latest information.

Course Content

25 Lectures
10 Quizzes
24 Hands-on Labs

20 Hours
lessons and hands-on labs

Module 00: Introduction to the course

	Lessons	Duration
	Lesson 01: Course Introduction	05:28

Module 01: Introduction to AI on Azure

In this module, you will learn about artificial intelligence and related areas of computer science, such as data science and machine learning. You will also explore some considerations for responsibly building AI solutions and some of the key Azure services you can use to integrate AI capabilities into your applications.

	Lessons	Duration
	Lesson 01: Introduction to AI	05:33
	Lesson 02: AI in Azure	04:06

	Labs	Duration
	Lab 01: Environment setup	14:45

Module 02: Developing AI Apps with Cognitive Services

This module teaches you how to provision, consume, secure, and monitor cognitive services. You will also learn how to deploy and use cognitive services containers.

	Lessons	Duration
	Lesson 01: Getting Started with Cognitive Services	04:33
	Lesson 02: Using Cognitive Services for Enterprise Applications	04:23

	Labs	Duration
	Lab 01: Get Started with Cognitive Services	08:00
	Lab 02: Manage Cognitive Services Security	12:49
	Lab 03: Monitor Cognitive Services	08:01
	Lab 04: Use a Cognitive Services Container	06:44

Module 03: Getting Started with Natural Language Processing

In this module, you will learn how to use the Text Analytics service to analyze text, and how to use the Translator service to translate text.

	Lessons	Duration
	Lesson 01: Analyzing Text	04:34
	Lesson 02: Translating Text	02:35

	Labs	Duration
	Lab 05: Analyze Text	10:17
	Lab 06: Translate Text	06:49

Module 04: Building Speech-Enabled Applications

In this module, you will learn how to use the Speech Service to recognize, synthesize, and translate speech.

	Lessons	Duration
	Lesson 01: Speech Recognition and Synthesis	04:54
	Lesson 02: Speech Translation	03:25

	Labs	Duration
	Lab 07 Recognize and Synthesize Speech	13:22
	Lab 08: Translate Speech	10:03

Module 05: Creating Language Understanding Solutions

In this module, you will learn how to use the Language Understanding Service to define a language model and predict intents and entities from user utterances.

	Lessons	Duration
	Lesson 01: Creating a Language Understanding App	04:43
	Lesson 02: Publishing and Using a Language Understanding App	04:05
	Lesson 03: Using Language Understanding with Speech	02:47

	Labs	Duration
	Lab 09: Create a Language Understanding App	30:38
	Lab 10: Create a Language Understanding Client Application	07:11
	Lab 11: Use the Speech and Language Understanding Services	09:56

Module 06: Building a QnA Solution

In this module, you will learn how to use the QnA Maker service to create a knowledge base of question-and-answer pairs that can support an application or bot.

	Lessons	Duration
	Lesson 01: Creating a QnA Knowledge Base	03:47
	Lesson 02: Publishing and Using a QnA Knowledge Base	03:43

	Labs	Duration
	Lab 12: Create a QnA Solution	16:04

Module 07: Conversational AI and the Azure Bot Service

In this module, you will learn how to use the Bot Framework SDK and Composer to implement conversational AI solutions.

	Lessons	Duration
	Lesson 01: Bot Basics	04:46
	Lesson 02: Implementing a Bot	03:46

	Labs	Duration
	Lab 13: Create a Bot with the Bot Framework SDK	10:09
	Lab 14: Create a Bot with Bot Framework Composer	29:06

Module 08: Getting Started with Computer Vision

In this module, you will learn how to use the Computer Vision service to analyze images, and the Video Analyzer service to analyze videos.

	Lessons	Duration
	Lesson 01: Analyzing Images and Videos	05:20

	Labs	Duration
	Lab 15: Analyze Images with Computer Vision	17:22
	Lab 16: Analyzing Videos	15:41

Module 09: Developing Custom Vision Solutions

In this module, you will learn how to use the Custom Vision service to create image classification and object detection models.

	Lessons	Duration
	Lesson 01: Image Classification	03:31
	Lesson 02: Object Detection	04:10

	Labs	Duration
	Lab 17: Classify Images with Custom Vision	13:17
	Lab 18: Detect Objects in Images with Custom Vision	13:12

Module 10: Detecting, Analyzing, and Recognizing Faces

In this module, you will learn how to use the Computer Vision and Face services to detect, analyze, and recognize faces.

	Lessons	Duration
	Lesson 01 Detecting Faces with the Computer Vision Service	03:49
	Lesson 02: Using the Face Service	03:34

	Labs	Duration
	Lab 19 Detect, Analyze, and Recognize Faces	20:51

Module 11: Reading Text in Images and Documents

In this module, you will learn how to use the Computer Vision Service to read text in images and documents, and how to use the Form Recognizer Service to extract data from forms.

	Lessons	Duration
	Lesson 01: Reading text and Extracting Information from Forms	05:24

	Labs	Duration
	Lab 20: Read Text in Images	07:27
	Lab 21: Extract Data from Forms	12:50

Module 12: Creating a Knowledge Mining Solution

In this module, you will learn how to use Azure Cognitive Search to build an AI-powered solution for search and knowledge mining.

	Lessons	Duration
	Lesson 01: Implementing an Intelligent Search Solution	03:52
	Lesson 02: Developing Custom Skills for an Enrichment Pipeline	02:50
	Lesson 03: Creating a Knowledge Store	02:24

	Labs	Duration
	Lab 22: Create an Azure Cognitive Search Solution	24:01
	Lab 23: Create a Custom Skill for Azure Cognitive Search	23:51
	Lab 24: Create a Knowledge Store with Azure Cognitive Search	14:29

About the Instructor

Umar Farooq ( MS Azure AI Engineer )

Microsoft Certified Azure AI Engineer Associate

I'm a Microsoft Certified Azure AI Engineer Associate holding a Bachelor's degree in Mechatronics Engineering. In addition to my extensive experience in Artificial Intelligence and Machine Learning, I have worked on projects on Microsoft Azure and Python for almost 2 years. I enjoy working on emerging AI technologies and having productive conversations about them. If you love doing the same, join me on this exciting journey of becoming a certified Azure AI Engineer.

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

Plan and manage an Azure Cognitive Services solution (15-20%)
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, Languages, Speech, Form Recognizer)
- Deploy Cognitive Services Containers in Microsoft Azure
Implement Computer Vision solutions (20-25%)
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 or PDFs by using the Computer Vision service
- Extract information using pre-built models 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
- 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 Azure Video Analyzer for Media (formerly 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
Implement natural language processing solutions (20-25%)
Analyze text by using the Language service
- Retrieve and process key phrases
- Retrieve and process entity information (people, places, urls)
- 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
- Improve text-to-speech accuracy
- Implement intent recognition
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 a initial language model by using Language Understanding Service (LUIS)
- Create intents and entities based on a schema, and 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 Language Understanding
- 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 Language Understanding 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
Create a Questions Answering solution using the Language service
- Create a question answering project
- Import questions and answers
- 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
Implement knowledge mining solutions (15-20%)
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
Implement conversational AI solutions (15-20%)
Design and implement conversation flow
- Design conversation logic for a bot
- Create and evaluate *.chat file conversations by using the Bot Framework Emulator
- Choose an appropriate conversational model for a bot, including activity handlers and dialogs
Create a bot by using the Bot Framework SDK
- Use the Bot Framework SDK to create a bot from a template
- Implement activity handlers and dialogs
- Use Turn Context
- Test a bot using the Bot Framework Emulator
- Deploy a bot to Azure
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
- Publish a bot
- Add language generation for a response
- Design and implement adaptive cards
Integrate Cognitive Services into a bot
- Integrate a question answering model
- Integrate a LUIS service
- Integrate a Speech service resource

Frequently Asked Questions

Does the AI-102T00 course have hands-on labs?

Yes, the AI-102T00 course has hands-on practical labs for all the modules to practice
What are the working hours for support team?

Support Team  is available for assistance 8 hours a day (from Monday to Friday)
What is the meaning of ‘enhanced content’?

Instructor Brandon's 'Enhanced Content' offers an exclusively designed course increase in the official Microsoft Curriculum, adding real-world changes that resonate with today's software updates.
Is there any demo available?

Yes, demonstrations are available as reference, showing the steps to achieve solutions so that you do not get stuck during the hands-on component.
What is the refund policy for the course?

There is no refund granted once the course is purchased as we provide all the course contents upon purchasing. All completed purchases are final and non-refundable. For more details, click here
How long do I have access to the course?

You have access to your purchased course for 1 months: 30 days for lectures and 30 days for labs.
Are there any special licenses being offered during training?

We provide our students with the licenses required to do the course and the labs.
How many hours will it take to complete AI-102T00 course?

We have approximately  20hrs of course content which includes 5hrs of video lessons, power point presentations and quizzes and 15hrs of labs.
How many hours should I spend each week on the course?

We suggest around 20hrs a week in order to complete this course on time.
How do we extend lab time beyond 30 days?

An ‘Extension Fee’ covers all the costs of your licenses and support, so for a fee of $400, you can extend your lab time

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

Instructor-led course to gain the skills needed to become a Microsoft Certified AI Engineer

Buy Now

Prominent Features

20 hours of course content
Virtual Machine and Licenses for Labs
25 Lectures, 24 Labs and 10 Quizzes
Dedicated support (8 hours a day)
Course completion certification

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

What you will learn

Requirements

Description

Prominent Features

Proprietary Course Enhancements

About the Instructor

Umar Farooq ( MS Azure AI Engineer )

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

Select the appropriate Cognitive Services resource

Plan and configure security for a Cognitive Services solution

Create a Cognitive Services resource

Plan and implement Cognitive Services containers

Analyze images by using the Computer Vision API

Extract text from images

Extract facial information from images

Implement image classification by using the Custom Vision service

Implement an object detection solution by using the Custom Vision service

Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer)

Analyze text by using the Language service

Manage speech by using the Speech service

Translate language

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

Iterate on and optimize a language model by using Language Understanding

Manage a Language Understanding model

Create a Questions Answering solution using the Language service

Implement a Cognitive Search solution

Implement an enrichment pipeline

Implement a knowledge store

Manage a Cognitive Search solution

Manage indexing

Design and implement conversation flow

Create a bot by using the Bot Framework SDK

Create a bot by using the Bot Framework Composer

Integrate Cognitive Services into a bot

Frequently Asked Questions

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

Prominent Features

If you have any questions contact us at response@instructorbrandon.com

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