Microsoft Azure AI Engineer Associate Boot Camp

DreamsPlus provides a thorough Microsoft Azure AI Engineer Associate Boot Camp that is accessible online and in Chennai. To ensure your success in the AI Engineer certification process, we created this program to provide you with real-world experience and prepare you for the Microsoft Azure AI Engineer Certification.

Syllabus 

Program Overview:

• Plan and manage an Azure AI solution (25–30%)
• Implement image and video processing solutions (15–20%)
• Implement natural language processing solutions (25–30%)
• Implement knowledge mining solutions (5–10%)
• Implement conversational AI solutions (15–20%)

Plan and manage an Azure AI solution (25–30%)

Select the appropriate Azure AI service:

• Choose the right provider for a vision solution.
• Choose the right language analysis service provider.
• Choose the right decision support solution service.
• Choose the suitable speech solution provider.
• Choose the relevant Applied AI services. 

Plan and configure security for Azure AI services

• Control account keys
• Oversee resource authentication.
• Utilize Azure Virtual Networks to provide secure services.
• Plan for a solution that complies with Responsible AI guidelines.

Create and manage an Azure AI service

• Establish an Azure AI resource.
• Set up diagnostic logging 
• control Azure AI service expenses.
• Keep an eye on an Azure AI resource 

Deploy Azure AI services

• Establish a service’s default endpoint.
• Utilize the Azure portal to create a resource.
• Connect Azure AI services to a pipeline for continuous deployment and integration (CI/CD) pipeline
• Arrange for the deployment of containers.
• Use prebuilt containers in a networked setting. 

Create solutions to detect anomalies and improve content

• Provide a solution that makes use of Anomaly Detector, a part of Cognitive Services
• Provide a solution utilizing the Azure Content Moderator, a part of Cognitive Services
• Create a solution that uses Personalizer, part of Cognitive Services
• Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services
• Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services

Implement image and video processing solutions (15–20%)

Analyze images

• Select appropriate visual features to meet image processing requirements
• Create an image processing request to include appropriate image analysis features
• Interpret image processing responses

Extract text from images

• Extract text from images or PDFs by using the Computer Vision service
• Convert handwritten text by using the Computer Vision service
• Extract information using prebuilt models in Azure Form Recognizer
• Build and optimize a custom model for Azure Form Recognizer

Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services

• Choose between image classification and object detection models
• Specify model configuration options, including category, version, and compact
• Label images
• Train custom image models, including classifiers and detectors
• Manage training iterations
• Evaluate model metrics
• Publish a trained iteration of a model
• Export a model to run on a specific target
• Implement a Custom Vision model as a Docker container
• Interpret model responses

Process videos

• Process a video by using Azure Video Indexer
• Extract insights from a video or live stream by using Azure Video Indexer
• Implement content moderation by using Azure Video Indexer
• Integrate a custom language model into Azure Video Indexer

Implement natural language processing solutions (25–30%)

Analyze text

• Retrieve and process key phrases
• Retrieve and process entities
• Retrieve and process sentiment

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

• Detect the language used in text
• Detect personally identifiable information (PII)

Process speech

• Implement and customize text-to-speech
• Implement and customize speech-to-text
• Improve text-to-speech by using SSML and Custom Neural Voice
• Improve speech-to-text by using phrase lists and Custom Speech
• Implement intent recognition
• Implement keyword recognition

Translate language

• Translate text and documents by using the Translator service
• Implement custom translation, including training, improving, and publishing a custom model
• Translate speech-to-speech by using the Speech service
• Translate speech-to-text by using the Speech service
• Translate to multiple languages simultaneously

Build and manage a language understanding model

• Create intents and add utterances
• Create entities
• Train evaluate, deploy, and test a language understanding model
• Optimize a Language Understanding (LUIS) model
• Integrate multiple language service models by using Orchestrator
• Import and export language understanding models

Create a question answering solution

• Create a question answering project
• Add question-and-answer pairs manually
• Import sources
• 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
• Create a multi-language question answering solution
• Create a multi-domain question answering solution
• Use metadata for question-and-answer pairs

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

Implement knowledge mining solutions (5–10%)

Implement a Cognitive Search solution

• Provision a Cognitive Search resource
• Create data sources
• Define an index
• Create and run an indexer
• Query an index, including syntax, sorting, filtering, and wildcards
• Manage knowledge store projections, including file, object, and table projections

Apply AI enrichment skills to an indexer 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 incremental enrichment

Implement conversational AI solutions (15–20%)

Design and implement conversation flow

• Design conversational logic for a bot
• Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot

Build a conversational bot

• Create a bot from a template
• Create a bot from scratch
• Implement activity handlers, dialogs or topics, and triggers
• Implement channel-specific logic
• Implement Adaptive Cards
• Implement multi-language support in a bot
• Implement multi-step conversations
• Manage state for a bot
• Integrate Cognitive Services into a bot, including question answering, language understanding,

Test, publish, and maintain a conversational bot

• Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app
• Test a bot in a channel-specific environment
• Troubleshoot a conversational bot
• Deploy bot logic