Master Azure Artificial Intelligence (AI, ML, GenAI)

Explore Azure AI vision for practical computer vision with image analysis and text extraction. Learn to analyze images and video, generate captions and tags, and detect faces for app integration.

Learn to use Azure AI Vision Face API for detection, recognition, and liveness. Train with a person group and honor privacy, anonymous ids kept 24 hours, with restricted access approvals.

Explore Azure Custom Vision for image classification and object detection, label data with COCO files, train with CNN or transformer models, and deploy via Azure ML and Vision resources.

Explore video analysis with Azure Video Indexer, including facial recognition, OCR, speech transcription, and scene segmentation, with custom and predefined models.

Learn to build smart apps with Azure Cognitive Services, applying language models to text analysis, translation, question answering, conversational language understanding, custom classification, named entity extraction, speech recognition, and synthesis.

Analyze texts with language detection, key phrase extraction, sentiment analysis, named entity recognition, entity linking, summarization, and PII detection, plus translation.

Explore translating text with the Microsoft Translator API, covering language detection, one-to-many translation, transliteration, and a custom translation model via the translator portal.

Learn to build a question answering solution with Azure AI Language by creating, training, testing, and publishing a knowledge base in Language Studio, enabling multi-turn conversations and active learning.

Provision an Azure AI language resource to define intents, entities, and utterances for a conversational language understanding app, using pre-built components, then train, test, publish, and review the model.

Learn to build custom classification and named entity recognition models in Azure language service by labeling data, training, deploying, evaluating precision, and integrating via the language API.

Explore speech recognition, translation and synthesis with Azure AI speech service, provisioning resources, implementing speech to text and text to speech, and using SSML for voice customization.

Analyze and translate text to build a conversation language understanding model and a question answering solution, plus speech recognition, synthesis, and translation, connect an app to Azure AI language resources.

Discover the core components of an Azure AI search solution, understand how an enrichment pipeline works, and practice building a knowledge mining solution with custom skills and a knowledge store.

Implement an intelligent search solution with Azure AI Search, indexing enriched data from blob storage and JSON documents into a semantic, knowledge mining index.

Explore knowledge mining with Azure AI search, learn to set up a custom search solution, and create a knowledge store with diverse projections for organized data access.

Master Azure AI document intelligence using pre-built models to read text, extract structure and key-value pairs from invoices and memos, call the API SDK, and train custom models.

Explore Azure's prebuilt document intelligence models by analyzing a receipt example, using REST API or SDKs, and retrieving layout, pages, lines, and words in JSON via a poller.

Explore custom models in document intelligence, mastering custom classification and extraction, training with labeled examples, project setup with storage integration, labeling workflows, and assessing accuracy and confidence scores.

Develop generative AI solutions with Azure OpenAI service by getting started, building applications, and applying prompt engineering with your own data.

Learn to deploy and manage Azure OpenAI service and Studio, select models, tailor prompts, and monitor quotas, with features like copilots, embeddings, and code interpreter.

Integrate Azure OpenAI into your app by using completions, embeddings, and chat completion endpoints, convert text to vectors for semantic search, and implement with the Azure OpenAI SDK.

Explore prompt engineering with Azure OpenAI Service, learning how to craft clear instructions, tailor prompts for different endpoints, and use grounding content, templates, and system messages to shape model outputs.

Learn to implement retrieval-augmented generation with Azure OpenAI by connecting your data source, indexing it in Azure Search as a vector store, and querying via SDK or REST API.

Explore Azure AI Studio, a robust environment for AI innovation and secure, enterprise-grade model deployment. Access model catalog, promo flow, governance, monitoring, privacy, and content safety to build responsible AI.

Explore the architecture of AI Studio within Azure Machine Learning, including managed VNet and private endpoints, and learn to deploy prompt flows to OpenAI and other AI services.

Clone the open-source deployment from GitHub, follow the readme steps to configure and deploy the infrastructure with Azure CLI, generate a certificate, and customize parameters for the first deployment.

Deploy Azure resources by creating a resource group in your region, monitor deployment, and retry on errors. Walkthrough covers the baseline AI architecture with application gateway, private links, and endpoints.

Approve the shared private links to enable private communication between AI search and other services, and configure private endpoint connections to index data from AI studio using OpenAI embeddings.

Deploy a jump box Bastion to enable private connectivity to a VM through private endpoints. Test access by launching Azure Studio inside the portal with VM admin credentials and MFA.

Connect to the ai studio hub to enable private connectivity and deploy and test GPT-4 or another model. Use secure, keyless authentication and explore marketplace models from OpenAI and Microsoft.

Add your own data by indexing a PDF, creating a data source in a project, and using embeddings with AI search to query your index in the playground.

Master Azure artificial intelligence teaches building multi-step prompts with PromptFlow, an LLM orchestration tool that combines data lookup, custom Python prompts, and context-aware chat to deploy AI workflows as APIs.