DESIGNING AND IMPLEMENTING A MICROSOFT AZURE AI SOLUTION – AI102

DESIGNING AND IMPLEMENTING A MICROSOFT AZURE AI SOLUTION – AI102 TRAINING IN PUNE | ONLINE

Plan and Manage an Azure AI Solution

Select the appropriate Azure AI Service

– Select the appropriate service for a vision solution
– Select the appropriate service for a language analysis solution
– Select the appropriate service for a decision support solution
– Select the appropriate service for a speech solution
– Select the appropriate Applied AI services

Plan and configure security for Azure AI Services

– Manage account keys
– Manage authentication for a resource
– Secure services by using Azure Virtual Networks
– Plan for a solution that meets responsible AI principles

Create and manage an Azure AI service

– Create an Azure AI resource
– Configure diagnostic logging
– Manage costs for Azure AI services
– Monitor an Azure AI resource

Deploy Azure AI services

– Determine a default endpoint for a service
– Create a resource by using the Azure portal
– Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline
– Plan a container deployment
– Implement prebuilt containers in a connected environment

Create solutions to detect anomalies and improve content

– Create a solution that uses Anomaly Detector, part of Cognitive Services
– Create a solution that uses Azure Content Moderator, 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

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 image classification and object detection
– Manage training iterations
– Evaluate model metrics
– Publish a trained 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

Analyze text

– Retrieve and process key phrases
– Retrieve and process entities
– Retrieve and process sentiment
– 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 an orchestration workflow
– 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

Implement Knowledge Mining Solutions

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

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, and Speech service

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