Official Technical Resources: Microsoft (page 9)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 121–135.
Compress vectors using scalar or binary quantization
Configure built-in scalar or quantization for compressing vectors on disk and in memory.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Create a vector query in Azure AI Search
Learn how to create vector queries that target an index in Azure AI Search.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Eliminate optional vector instances from storage
In vector search, configure storage to exclude optional copies of vector fields, reducing the storage requirements of vector data.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Truncate dimensions using MRL compression
Truncate dimensions on text-embedding-3 models using Matryoshka Representation Learning (MRL) compression.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Vector index size and limits
Learn about the factors that affect the size of a vector index.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Integrated vector embedding in Azure AI Search
Learn how integrated vectorization in Azure AI Search automatically chunks and generates embeddings during indexing and query execution using built-in skills.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Multi-vector field support in Azure AI Search (preview)
Learn how Azure AI Search enables multi-vector field support for better search experiences with long-form or multimodal content.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Vector search in Azure AI Search
Learn about vector search in Azure AI Search for similarity matching across text, images, and multilingual content using numeric embeddings and vector indexes.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Relevance in vector search
Learn about the concepts behind vector relevance, scoring, including how matches are found in vector space and ranked in search results.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Custom Web API vectorizer
Use the Custom Web API vectorizer to integrate your custom code for generating embeddings at query time.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Vector indexes in Azure AI Search
Learn how Azure AI Search stores and manages vector indexes for similarity search, including vector field types, algorithms, and storage requirements.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Retrieval and Semantic Search · Retrieval-Augmented Generation (RAG) · Vector Databases
Link last checked: 2026-09-24
Forecasting at scale: many models and distributed training
Learn about different ways to scale forecasting model training
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Supervised Learning · Recurrent Neural Network (RNN)
Link last checked: 2026-09-24
Calendar features for time series forecasting in AutoML
Learn how Azure Machine Learning's AutoML creates calendar and holiday features
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Supervised Learning · Recurrent Neural Network (RNN)
Link last checked: 2026-09-24
Deep learning with AutoML forecasting
Learn how Azure Machine Learning's AutoML uses deep learning to forecast time series values
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Supervised Learning · Recurrent Neural Network (RNN)
Link last checked: 2026-09-24
Inference and evaluation of forecasting models
Learn about different ways to inference and evaluate forecasting models using Azure Machine Learning.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Model Evaluation and Benchmarks · Large Language Model (LLM)
Link last checked: 2026-09-24