В течение нескольких месяцев меня очень интересует машинное обучение в целом и особенно открытый ИИ, я решил воспользоваться этой мотивацией, чтобы углубить свои знания в области машинного обучения.
Я начал с курса на Udemy для машинного обучения, курс был отличный, правда, очень длинный, к сожалению, я только что закончил 40% курса.
Чтобы подбодрить себя, я поставил перед собой цель: я решил учиться, чтобы пройти сертификацию Microsoft по ИИ, которая не самая простая: AI-102 Designing and Implement a Microsoft Azure AI Solution.
Готовясь к этой сертификации, я просто перечисляю все необходимые знания, которые мне нужны для получения сертификации. Позвольте мне показать вам карту разума.
--- AI 102: colorFreezeLevel: 1 markmap: maxWidth: 300 initialExpandLevel: 3 --- ## Plan and Manage an Azure AI Solution (25–30%) - 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 (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 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 (25–30%) - 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 (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, 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
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