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Certified Microsoft Azure AI Engineer badge achieved after attending the AI-102 Azure AI Engineer Course & Certification
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Microsoft Certified Azure AI Engineer (AI-102) course

Become an AI expert on Azure. Learn to design and implement AI solutions for optimal business transformation and innovation.

course: Microsoft Certified Azure AI Engineer (AI-102)

Duration: 4 days

Format: Virtual or Classroom

prepare-exam Prepares for Exam : Designing and Implementing a Microsoft Azure AI Solution (AI-102)

certification-icon Prepares for Certification : Microsoft Certified: Azure AI Engineer Associate

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Overview

Unlock the power of AI in Azure with our specialized training course. Learn to design and implement AI solutions using Azure AI technologies. From natural language processing to computer vision, this course covers all aspects of AI engineering. With hands-on labs and expert instruction, you'll gain the skills needed to pass the AI-102 exam and become certified as a Microsoft Azure AI Engineer. Enroll now and take your career to new heights with our comprehensive AI training and certification course.

This course includes
  • intructor-iconInstructor-led training
  • intructor-iconPractice test
  • intructor-iconPre-reading
  • intructor-iconPersonal Learning Path
  • intructor-iconCertification Guarantee
  • intructor-iconEmail, chat and phone support

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Who is this course for?

Who is the Microsoft Certified Azure AI Engineer (AI-102) training course for?

The Microsoft Certified Azure AI Engineer (AI-102) certification is for individuals with experience in designing and implementing AI solutions using Azure tools and services. The certification validates the ability to use Azure Cognitive Services, Machine Learning, and other AI technologies to build intelligent solutions. To obtain the certification, candidates must pass the AI-102 exam, which covers topics such as designing and implementing AI solutions, working with data storage and processing, building and deploying models using Azure Machine Learning, and natural language processing and computer vision technologies.

Curriculum

What you will learn during our Microsoft Certified Azure AI Engineer course.

  • 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
  • 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 prebuilt receipt model in Form
  • 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 texttospeech
  • Customize texttospeech
  • Implement speechtotext
  • Improve speechtotext accuracy
  • Translate text by using the Translator service
  • Translate speechtospeech by using the Speech service
  • Translate speechtotext 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 builtin 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 reindexing
  • 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 multiturn conversation
  • Add alternate phrasing
  • Add chitchat 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 bottohuman handoff
  • Troubleshoot a conversational bot
  • Add a custom middleware for processing user messages
  • Manage identity and authentication
  • Implement channelspecific 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

Preparation

How to best be prepared for our Microsoft Certified Azure AI Engineer course.

  • [Dictionary item: Orange-check] Proficiency in Microsoft Azure and its various services, especially those related to artificial intelligence and machine learning.
  • [Dictionary item: Orange-check] Understanding of fundamental AI concepts such as machine learning, natural language processing (NLP), computer vision, and conversational AI.
  • [Dictionary item: Orange-check] Experience in developing and deploying machine learning models using frameworks like TensorFlow, PyTorch, or Azure Machine Learning.
  • [Dictionary item: Orange-check] Knowledge of data preparation, feature engineering, model training, and evaluation techniques.
  • [Dictionary item: Orange-check] Familiarity with programming languages such as Python or R for data manipulation and model development.
  • [Dictionary item: Orange-check] Understanding of data storage and management solutions, including SQL and NoSQL databases.
  • [Dictionary item: Orange-check] Experience in deploying and managing applications in a cloud environment.
  • [Dictionary item: Orange-check] Proficiency in using development tools like Visual Studio or Azure DevOps for building and deploying AI solutions.
  • [Dictionary item: Orange-check] Strong problem-solving and analytical skills.
  • [Dictionary item: Orange-check] Completion of relevant training or certification courses on Azure fundamentals and AI concepts is recommended.

Meet our instructors

Meet some of the Readynez Instructors you can meet on your course. They are experts, passionate about what they do, and dedicated to give back to their industry, their field, and those who want to learn, explore, and advance in their careers.

tiago-costa

Julian Sharp

Julian is an official Microsoft Business Application MVP and one of the most widely recognized experts on Dynamics 365.

Julian has been with Readynez almost since the start 11 years ago.

He has been an MCT since 2007 and he is a subject matter expert in all aspects of Dynamics 365 Customer Engagement through advice, optimization and training.

Julian has worked with Dynamics since first release and has over the past years used Azure to enhance solutions for users.

He is an official Microsoft Business Application MVP and is one of the most widely recognized experts for Power Platform and Dynamics 365 within the Microsoft Community.

He works as a Consultant on large international projects and and has extensive hands-on experience as well as numerous certifications.

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Jens Gilges

Jens is a 20-year MCT, an Amazon Authorized Champion Instructor and a well accomplish Cloud Infrastructure Security Consultant and Penetration Tester.

Jens Gilges is a highly skilled professional with expertise in Azure, AWS, and Penetration Testing. With a remarkable 20-year tenure as a Microsoft Certified Trainer (MCT), Jens has honed his proficiency in various Microsoft technologies. Notably, he is not just a trainer but an industry leader, holding the prestigious title of AWS Champion Instructor.

Jens is dedicated to imparting his knowledge globally, delivering top-tier security and AWS training to clients across the world. His passion for these cloud platforms shines through in his engaging and informative sessions. Whether you're seeking insights into Azure's versatile capabilities, AWS's vast infrastructure, or the intricacies of Penetration Testing, Jens is your go-to expert.

With Jens at the helm, you can expect a comprehensive learning experience that combines years of expertise with a commitment to staying at the forefront of cloud technologies. Join him on a journey of continuous learning and explore the ever-evolving landscapes of Azure, AWS, and Penetration Testing.

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FAQ

FAQs for the Microsoft Certified Azure AI Engineer (AI-102) course.

Microsoft Certified Azure AI Engineer certification validates expertise in designing and implementing AI solutions on Azure. It demonstrates proficiency in leveraging Azure AI services to build intelligent applications and solve real-world business problems using artificial intelligence technologies.

Join our Microsoft Certified Azure AI Engineer course (AI-102) and unlock the potential to design and implement AI solutions on Azure. At Readynez, we provide the training and support you need to pass the exam and become certified in this essential field. Take your career to new heights in artificial intelligence with our expert guidance and hands-on learning approach.

Prerequisites for Microsoft Certified Azure AI Engineer certification include familiarity with Azure services, machine learning, and programming languages like Python. Experience in designing and implementing AI solutions, along with knowledge of Azure cloud infrastructure, is recommended for success in this certification.

The AI-102 exam is priced at approximately €140. This exam consists of 40-60 multiple-choice questions, and candidates are allotted a maximum of 130 minutes to complete it.

The AI-102 exam covers a wide range of topics related to Azure AI services, including Azure Cognitive Services, natural language processing, computer vision, and machine learning model deployment on Azure. It assesses candidates' abilities to design and implement AI solutions using various Azure technologies.

Yes, Microsoft Certified Azure AI Engineer certification is highly valuable for professionals seeking to advance their careers in AI development, data science, and cloud computing. It demonstrates expertise in designing and implementing AI solutions on Azure, opening up new opportunities for career growth and advancement.

The time it takes to become Microsoft Certified Azure AI Engineer certified varies depending on individual experience, study habits, and dedication. Typically, candidates spend a few months preparing for the AI-102 exam by studying relevant materials, practising with hands-on labs, and taking practice tests.

Yes, the AI-102 exam can be taken online from the comfort of your own home or office. Microsoft offers online proctoring options for many of its certification exams, including the AI-102 exam. Ensure your computer meets the technical requirements and follow the registration process to schedule your online exam session.

The difficulty of passing the AI-102 exam varies based on individual experience, preparation, and familiarity with the exam topics. Candidates with a solid understanding of Azure AI services and machine learning concepts, combined with effective study strategies, are more likely to succeed in passing the exam.

The passing score for the AI-102 exam is 700 or greater (out of 1000).

Maintain your Microsoft Certified Azure AI Engineer certification by staying updated with the latest Azure AI technologies and completing recertification requirements as outlined by Microsoft. Participate in relevant training programs, attend conferences, and engage in continuous learning to keep your skills sharp and up-to-date.

Salary potential after obtaining Microsoft Certified Azure AI Engineer certification varies depending on factors such as location, industry, experience, and job role. Generally, professionals with Azure AI expertise command competitive salaries in fields such as AI development, data science, and cloud computing, offering excellent earning potential.

Reviews

Feedback from our Microsoft Certified Azure AI Engineer delegates.

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Stephen Ridgway

Readynez is the best training provider I've used for many years. Their customer service is first class, prices are very competitive and instruction excellent.

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Johan Andersson

Easy to attend over Teams and an excellent instructor gave me great value for the time I invested.

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