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AI and Machine Learning premium Data drift A measurable change in production input data or feature distributions compared with the baseline data used to train or validate a model. Model monitoring and MLOps Intermediate 4 commands Aliases: feature drift, input data drift, model data drift Quick peek Open full term page

AI and Machine Learning field-manual-ready Model monitoring Model monitoring watches deployed models for changes in inputs, outputs, quality, latency, errors, cost, and operational health. Azure Machine Learning intermediate 4 commands Aliases: ML model monitoring, model monitor, model monitoring, Azure Machine Learning monitoring, data drift Quick peek Open full term page

Analytics learning-path-anchor Databricks model serving A managed serving endpoint pattern for exposing Databricks models or AI workloads for online inference with operational controls. Databricks intermediate 4 commands Aliases: Databricks Model Serving, serving endpoint, model serving endpoint Quick peek Open full term page

AI and Machine Learning field-manual-ready Model A model is a trained or selected AI artifact that performs predictions, generation, classification, embeddings, or another inference task. Generative AI fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model drift Model drift is the loss of model usefulness when production data, behavior, labels, or real-world patterns change after training. Azure Machine Learning fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model evaluation Model evaluation measures a model against defined data, tasks, metrics, thresholds, safety checks, and business expectations. Azure Machine Learning intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model lifecycle Model lifecycle is the full operating path from model idea and training through evaluation, deployment, monitoring, retraining, retirement, and replacement. Microsoft Foundry intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Azure SQL Database DTU model Azure SQL Database DTU model is a purchasing model that bundles compute, storage, and I/O resources into Database Transaction Units for Basic, Standard, and Premium tiers. Azure SQL Database fundamentals 5 commands Aliases: Azure SQL Database DTU model, DTU model, DTU-based purchasing model, Database Transaction Unit, eDTU Quick peek Open full term page

AI and Machine Learning premium Invoice model Invoice model controls how teams automate invoice data extraction before routing results into approvals, ERP systems, payment workflows, audits, or exception handling queues. Teams see it in document intelligence studio, prebuilt invoice api calls. It is not a custom document model, a receipt model, an invoice section in billing, an accounting system, or a generative model that summarizes documents; confusing them can create incorrect payment amounts, missed line items. Use the term when reviewing access, monitoring, cost, recovery, or performance. It keeps architects, operators, security reviewers, and support teams focused on the same setting, resource, or behavior. Document Intelligence Intermediate 5 commands Aliases: prebuilt invoice model, Document Intelligence invoice model, invoice extraction model, invoice processing model Quick peek Open full term page

AI and Machine Learning premium Abuse monitoring Abuse monitoring is the safety layer that looks for harmful or policy-violating use of AI services. It is not a performance feature; it exists to help detect misuse, protect the service, and support responsible operation of model deployments. Azure OpenAI intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Machine Learning model registry A Machine Learning model registry organizes registered, versioned model assets in Azure Machine Learning. It helps teams track trained models, metadata, lineage, tags, versions, and deployment candidates so MLOps workflows can promote or roll back models safely. That context supports safer operational decisions. Machine learning advanced 4 commands Aliases: Azure ML model registry, ML model registry Quick peek Open full term page

AI and Machine Learning premium Model deployment Model deployment is the step where a trained or selected model becomes something an application can actually call. The model may have performed well in experiments, but deployment decides how it is hosted, secured, scaled, monitored, and routed. Generative AI fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Registered model A registered model is the production-ready record of a trained machine learning model. It is not just a file sitting in storage; it has a name, version, type, metadata, tags, and links back to training outputs. Registration gives teams a stable way Azure Machine Learning intermediate 5 commands Aliases: Azure ML registered model, model asset, versioned ML model, MLflow registered model Quick peek Open full term page

AI and Machine Learning field-manual-ready ML model registry An ML model registry stores, versions, documents, and governs trained model assets used for Azure Machine Learning deployment and review. Azure Machine Learning intermediate 4 commands Aliases: Azure ML model registry, model asset registry, registered ML model Quick peek Open full term page

AI and Machine Learning field-manual-ready Model benchmark A model benchmark measures model behavior on defined tasks, datasets, metrics, or leaderboards so teams can compare choices. AI platform and search intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model card A model card documents a model’s intended use, capabilities, limitations, provider context, evaluations, and deployment considerations. Microsoft Foundry intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model catalog A model catalog is a discovery experience for finding AI models by provider, task, modality, capability, benchmark, or deployment option. Microsoft Foundry intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model deployment type Model deployment type describes the hosting, traffic, region, and billing pattern used when a model is made available for inference. AI platform and search intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model lineage Model lineage traces how a model version was created, evaluated, approved, deployed, monitored, and changed over time. Azure Machine Learning fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-ready Model registry A model registry stores named, versioned machine learning models so teams can govern deployment, approval, rollback, and reuse. Azure Machine Learning fundamentals 4 commands Aliases: registered model registry, model registry, registered model, Azure Machine Learning registry Quick peek Open full term page

AI and Machine Learning field-manual-ready Model router A model router sends each prompt to an appropriate underlying model so teams can balance quality, latency, and cost. Microsoft Foundry Models intermediate 4 commands Aliases: Foundry model router, automatic model routing, model router, prompt routing Quick peek Open full term page

AI and Machine Learning field-manual-ready Model version A model version is a specific numbered or named instance of a model artifact used for deployment, comparison, rollback, and audit evidence. Azure Machine Learning fundamentals 4 commands Aliases: registered model version, model version, Azure ML model version Quick peek Open full term page

Databases premium DTU model The DTU model is an Azure SQL Database purchasing model that bundles compute, memory, and I/O into service tiers and service objectives such as Basic, Standard, and Premium. Azure SQL fundamentals 6 commands Aliases: DTU purchasing model, Azure SQL DTU model Quick peek Open full term page

AI and Machine Learning premium Document Intelligence custom model A Document Intelligence custom model is a trained model built from representative labeled documents to extract fields from organization-specific document types. Document Intelligence intermediate 5 commands Aliases: custom extraction model, custom neural model, custom template model, trained custom document model Quick peek Open full term page

AI and Machine Learning premium Document Intelligence layout model The Document Intelligence layout model is a prebuilt model that extracts document text, tables, selection marks, structure, and layout information without custom training. Document Intelligence fundamentals 5 commands Aliases: layout model, prebuilt layout model, document layout analysis, layout analysis model Quick peek Open full term page

AI and Machine Learning premium Document Intelligence model A Document Intelligence model is a prebuilt, custom, composed, or classifier model used to analyze documents and return structured extraction or classification results. Document Intelligence fundamentals 5 commands Aliases: document model, prebuilt model, custom model, model ID Quick peek Open full term page

AI and Machine Learning premium Foundry Models Foundry Models is the Microsoft Foundry model catalog and deployment experience for discovering, evaluating, and using cloud AI models through managed endpoints. Microsoft Foundry intermediate 5 commands Aliases: Microsoft Foundry Models, Foundry model catalog, AI Foundry models, model catalog Quick peek Open full term page

Web premium In-process worker model In-process worker model is the Azure Functions .NET execution model where function code runs in the same process as the Functions host. Azure Functions intermediate 5 commands Aliases: In-process worker model, in process worker model, in-process worker model, in-process-worker-model Quick peek Open full term page

Web premium Isolated worker model Isolated worker model is the Azure Functions execution model where .NET function code runs in a separate worker process instead of inside the Functions host process. Azure Functions intermediate 5 commands Aliases: .NET isolated worker, Azure Functions isolated process, dotnet-isolated, out-of-process worker, isolated Functions worker Quick peek Open full term page

Databases premium Azure SQL vCore model The Azure SQL vCore purchasing model lets customers independently choose compute, storage, service tier, and hardware configuration for Azure SQL Database workloads. Azure SQL Database intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Custom document model an Azure AI Document Intelligence model trained or built for a team’s specific document layout, fields, and extraction needs. Document Intelligence fundamentals 4 commands Aliases: Document Intelligence custom model, custom extraction model, custom neural document model Quick peek Open full term page

Analytics premium Data Factory monitoring The operational view of Data Factory pipeline runs, activity runs, trigger history, integration runtime health, metrics, logs, and alerts. Data integration and orchestration Intermediate 4 commands Aliases: Data Factory monitoring, ADF monitoring, data factory monitoring Quick peek Open full term page

AI and Machine Learning premium Embedding model An embedding model converts text into numerical vector form so applications can perform text similarity, retrieval, and other semantic comparison tasks. Azure OpenAI intermediate 4 commands Aliases: text embedding model, Azure OpenAI embedding model, vector embedding model Quick peek Open full term page

AI and Machine Learning premium Embeddings model An embeddings model is a deployed model used to convert text into vector representations for semantic similarity, retrieval, and vector search workloads. Azure OpenAI intermediate 4 commands Aliases: embedding model deployment, embeddings deployment, text embeddings model Quick peek Open full term page

AI and Machine Learning premium Fine-tuned model Fine-tuned model is a customized model produced from a supported base model by a completed fine-tuning job and then deployed for inference in Azure. Azure AI Foundry and Azure OpenAI intermediate 4 commands Aliases: Fine-tuned model, fine tuned model Quick peek Open full term page

AI and Machine Learning premium ID document model ID document model is a prebuilt Azure AI Document Intelligence model that extracts fields from identity documents such as passports and driver licenses. Document Intelligence intermediate 4 commands Aliases: id document model, ID document model, Identity document model, Prebuilt ID document model, prebuilt-idDocument Quick peek Open full term page

AI and Machine Learning premium Image generation model Image generation model is an Azure OpenAI or Foundry model deployment that creates images from prompts, commonly using the current gpt-image model family. Azure OpenAI intermediate 4 commands Aliases: Azure OpenAI image model, gpt-image model, image generation model, Image generation model, text-to-image model Quick peek Open full term page

AI and Machine Learning premium Composed document model an Azure AI Document Intelligence model that combines several custom document models behind one model identifier so different document types can be analyzed together Document Intelligence Intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-complete Layout model A layout model in Azure AI Document Intelligence analyzes a document structure and returns extracted text, tables, selection marks, paragraphs, roles, and layout information. Microsoft Learn describes layout capabilities for prebuilt extraction, Markdown or text output, and search enrichment scenarios that need structured document content. Document Intelligence fundamentals 12 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-complete Prebuilt document model A prebuilt document model is a ready-made Document Intelligence model for a common document type such as an invoice, receipt, ID document, contract, bank statement, pay stub, or tax form. Instead of collecting labeled examples and training a custom model, a developer calls the prebuilt model and receives extracted fields, text, tables, confidence scores, and layout information. It is useful when the document matches a known pattern well enough. It is not magic; teams still need validation, exception handling, data protection, and downstream business rules. Document Intelligence fundamentals 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Read model In Azure AI Document Intelligence, the Read model is the prebuilt OCR model that extracts printed and handwritten text, words, lines, bounding locations, and languages from documents and images. It is also the text foundation used by layout, prebuilt, and custom document models. Document Intelligence fundamentals 5 commands Aliases: Document Intelligence Read model, prebuilt-read, Read OCR, OCR read model, Azure AI Read model Quick peek Open full term page

AI and Machine Learning verified Receipt model The Document Intelligence receipt model is a prebuilt model that uses OCR and deep learning to analyze sales receipts and return structured fields such as merchant, transaction date, tax, total, and line-item details. It supports printed and handwritten receipts in varying formats and quality. Document Intelligence intermediate 5 commands Aliases: prebuilt receipt model, Document Intelligence receipt model, receipt data extraction, prebuilt-receipt Quick peek Open full term page

Databases field-manual-complete vCore model The vCore model is the Azure SQL purchasing model that separates compute and storage decisions for clearer sizing and cost planning. Azure SQL intermediate 4 commands Aliases: vCore purchasing model, Azure SQL vCore model, vCore-based purchasing model, SQL vCore model Quick peek Open full term page

Monitoring and Observability field-manual-ready Monitoring Reader Monitoring Reader is an Azure role that lets users view monitoring data and settings without broad resource management permissions. Azure Monitor RBAC fundamentals 4 commands Aliases: Azure Monitor Reader, Monitoring Reader, monitoring RBAC Quick peek Open full term page

Analytics learning-path-anchor Databricks MLflow The managed MLflow experience in Azure Databricks for experiments, runs, metrics, artifacts, model lineage, and MLOps evidence. Databricks fundamentals 4 commands Aliases: MLflow on Databricks, Databricks experiment tracking, MLflow tracking Quick peek Open full term page

Integration verified Receive and delete Receive and delete is an Azure Service Bus receive mode where the broker considers a message settled as soon as it sends the message to the receiver. If transfer or processing fails afterward, the message is lost instead of being redelivered. Azure Service Bus intermediate 5 commands Aliases: ReceiveAndDelete, receive-and-delete mode, destructive read, at-most-once receive Quick peek Open full term page

Integration verified Receive-and-delete mode A integration pattern or service capability in Messaging that helps teams connect services reliably without tightly coupling every component with clearer ownership, safety, and operational context. Messaging fundamentals 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Azure Machine Learning Azure Machine Learning is a cloud service for managing the machine learning lifecycle, including workspaces, data, training jobs, model deployment, monitoring, and MLOps. Machine learning operations advanced 5 commands Aliases: Azure ML, AML, Machine Learning workspace Quick peek Open full term page

Storage learning-path-anchor Data Lake storage account A storage feature or access model in Data Lake Storage Gen2 that helps teams store, protect, move, and govern application or analytics data with clearer ownership, safety, and operational context. Data Lake Storage Gen2 advanced 19 commands Aliases: No aliases yet Quick peek Open full term page

Databases command-rich Cosmos DB for Apache Gremlin Azure Cosmos DB for Apache Gremlin is a managed graph database service for storing, querying, and traversing graph data with the Gremlin language. Data platform intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page