Microsoft Azure AI-102
Master Azure AI with Tech Cryptors'
Microsoft Azure AI-102
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Tech Cryptors brings you the Microsoft Azure AI-102 course, an opportunity to delve into the intricacies of advanced Azure AI. Learn to design and implement complex AI solutions leveraging machine learning, natural language processing, and computer vision on the Microsoft Azure platform. Equip yourself to create intelligent applications that drive innovation, enhance user experiences, and provide valuable insights across diverse domains. Our cutting-edge courses are designed to equip aspiring professionals with practical skills and knowledge to excel in the dynamic world of networking.
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Understanding how to choose the appropriate Azure AI service for different scenarios in Microsoft Azure AI-102
Vision Solutions: Select the suitable service for vision-related tasks.
Language Analysis: Choose the right service for language analysis tasks.
Decision Support: Identify the appropriate service for decision support applications.
Speech Solutions: Select the right service for handling speech-related tasks.
Applied AI Services: Understand and utilize various applied AI services for specific needs.
Planning Security for Azure AI Services
Account Key Management: Learn to manage account keys securely.
Authentication: Understand how to manage authentication for your resources.
Azure Virtual Networks: Secure your services by integrating them with Azure Virtual Networks.
Responsible AI Principles: Plan for solutions that align with responsible AI principles to ensure ethical use.
Creating and Managing Azure AI Services
Getting started with Azure AI services involves these steps:
Azure AI Resource Creation: Create an Azure AI resource for your projects.
Diagnostic Logging Configuration: Configure diagnostic logging for better insights.
Cost Management: Learn how to manage costs associated with Azure AI services.
Deploying Azure AI Services in Microsoft Azure AI-102
Resource Monitoring: Monitor the performance of your Azure AI resources.
Deploying Azure AI services requires understanding deployment methods:
Default Endpoint Determination: Determine the default endpoint for a service.
Azure Portal Resource Creation: Create resources using the Azure portal.
CI/CD Integration: Integrate Azure AI services into continuous integration/continuous deployment pipelines.
Container Deployment: Plan and execute container deployments for Azure AI services.
Anomaly Detection and Content Improvement in Microsoft Azure AI-102
Anomaly Detection: Use Anomaly Detector from Cognitive Services to identify anomalies.
Content Moderation: Utilize Azure Content Moderator from Cognitive Services to moderate content.
Personalizer and Metrics Advisor: Implement solutions using Personalizer and Metrics Advisor from Applied AI Services.
Immersive Reader: Build solutions with Azure Immersive Reader from Applied AI Services.
Image Analysis and Text Extraction in Microsoft Azure AI-102
Understanding how to analyze images and extract text from them
Visual Feature Selection: Choose appropriate visual features for image processing tasks.
Image Analysis Request Creation: Create requests for image analysis with specified features.
Text Extraction: Extract text from images and PDFs using Computer Vision service.
Handwritten Text Conversion: Convert handwritten text using Computer Vision service.
Custom Image Models and Object Detection
Working with custom image models and object detection in Microsoft Azure AI-102
Image Classification vs. Object Detection: Decide between image classification and object detection models.
Model Configuration and Labeling: Specify model settings and label images for training.
Custom Model Training: Train and evaluate custom image models.
Model Management and Iterations: Manage training iterations, evaluate model metrics, and publish trained models.
Learn Text-to-Speech, Speech-to-Text, and Translation
Implementing various speech-related tasks with Azure services
Text-to-Speech and Speech-to-Text: Utilize Azure Speech service for converting text to speech and vice versa.
Custom Neural Voice and Custom Speech: Enhance text-to-speech and speech-to-text with customization.
Intent and Keyword Recognition: Implement intent and keyword recognition for speech understanding.
Building Language Understanding Models
Intents and Entities: Develop language understanding by creating intents and entities.
Training, Evaluation, and Deployment: Train, evaluate, and deploy language understanding models.
Optimization: Optimize Language Understanding models for better performance.
Integration and Export: Integrate models and export them for use in various applications.
Question Answering Solutions and Cognitive Search
Question Answering Projects: Develop projects for question answering applications.
Knowledge Base Management: Create, train, and publish knowledge bases for question answering.
Multi-Turn Conversations: Implement multi-turn conversations and alternate phrasing.
Cognitive Search: Provision, define indexes, run indexers, and query data for search solutions.
Conversational Bot Design and Deployment
Bot Design and Logic: Plan conversational logic and choose appropriate activity handlers, dialogs, and triggers.
Bot Creation: Create bots from templates or scratch, integrating Adaptive Cards and multi-language support.
Cognitive Services Integration: Incorporate Cognitive Services for enhanced bot capabilities.
Testing, Troubleshooting, and Deployment: Test, troubleshoot, and deploy bots, integrating them with CI/CD pipelines.
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