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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

AI and Machine Learning premium Machine Learning component A Machine Learning component is a reusable Azure Machine Learning pipeline building block with defined inputs, outputs, code, command, and environment. Components can be authored in YAML, Python, or designer workflows and reused across pipeline jobs to standardize training, evaluation, and deployment logic. Machine learning intermediate 4 commands Aliases: Azure ML component, ML component Quick peek Open full term page

AI and Machine Learning premium Machine Learning compute instance A Machine Learning compute instance is a managed cloud workstation inside Azure Machine Learning for notebooks, development, testing, and lightweight training. It gives one owner an integrated environment connected to the workspace, while administrators manage access, networking, schedules, and cost controls. Machine learning intermediate 4 commands Aliases: Azure ML compute instance, ML compute instance Quick peek Open full term page

AI and Machine Learning premium Machine Learning data asset A Machine Learning data asset is a named, versioned reference to data used by Azure Machine Learning jobs. It can point to files, folders, or MLTable definitions so teams can reuse training data without remembering raw storage paths. That context supports safer operational decisions. Machine learning intermediate 4 commands Aliases: Azure ML data asset, ML data asset Quick peek Open full term page

AI and Machine Learning premium Machine Learning environment A Machine Learning environment defines the software context for Azure Machine Learning training or inferencing. It captures packages, variables, Docker images, conda settings, and other runtime details so jobs can run reproducibly across compute targets. That context supports safer operational decisions. Machine learning intermediate 4 commands Aliases: Azure ML environment, ML environment Quick peek Open full term page

AI and Machine Learning premium Machine Learning job A Machine Learning job is a submitted unit of work in Azure Machine Learning, such as command, sweep, pipeline, or AutoML execution. Jobs run code on selected compute, consume inputs, produce outputs, and record logs, metrics, lineage, and status. That context supports safer operational decisions. Machine learning intermediate 4 commands Aliases: Azure ML job, ML job 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 Machine Learning workspace A Machine Learning workspace is the top-level Azure Machine Learning resource. It centralizes jobs, logs, metrics, outputs, models, environments, components, data assets, datastores, compute references, and collaboration settings so teams can build, train, track, and deploy machine learning work. That context supports safer operational decisions. Machine learning fundamentals 4 commands Aliases: Azure ML workspace, ML workspace Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry Create a governed project boundary where AI teams can build agents, evaluations, files, and model deployments without unmanaged sprawl.; Compare, evaluate, and approve model deployments before a generative AI feature is AI platform fundamentals 5 commands Aliases: AI Foundry, Microsoft Foundry, Foundry resource, Foundry project, Azure AI Foundry, azure ai foundry Quick peek Open full term page

Storage premium Azure Managed Lustre Azure Managed Lustre is a managed parallel file system for high-performance computing and AI workloads that need scalable, low-latency file storage in Azure. Parallel file systems advanced 5 commands Aliases: Managed Lustre, Azure Managed Lustre File System, AMLFS Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry hub Azure AI Foundry hub is the classic Foundry hub resource used to group AI projects that share common security, data access, connections, and platform settings. In Azure, teams encounter it when teams need hub-based projects for selected Foundry and Azure Machine Learning scenarios such as fine-tuning, shared connections, and custom ML work. The useful question AI platform advanced 4 commands Aliases: AI Hub, Foundry AI Hub, hub-based project, Azure AI hub Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry project An Azure AI Foundry project is the working area where a team builds and organizes an AI application inside Microsoft Foundry. It keeps related agents, evaluations, files, indexes, tools, connections, and model usage together instead of scattering them across a shared portal. Think of it as the project boundary for one AI product, prototype, or team. It is useful because AI work quickly becomes messy: prompts, test data, model deployments, safety checks, and access decisions all need a home with ownership and repeatable operations. AI platform advanced 4 commands Aliases: AI Foundry project, AI project, Foundry project, Microsoft Foundry project 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

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 premium Experiment An Experiment in Azure Machine Learning groups related runs so parameters, metrics, artifacts, models, and lineage can be tracked and compared. Teams use it to organize model training, evaluation, and tuning work so teams can compare runs and reproduce how a model version was produced. It is not a deployed endpoint, a registered model, a notebook file, a compute cluster, or proof that a model is fair, secure, or production-ready. In production, confirm workspace, experiment name, run IDs, parameters, metrics, artifacts, data version, environment, compute target, model registration, owner, and promotion criteria before treating the design as healthy or ready. Machine learning intermediate 6 commands Aliases: Azure ML experiment, MLflow experiment, machine learning experiment Quick peek Open full term page

AI and Machine Learning premium Feature store A Feature store is a managed repository for machine learning features that lets teams discover, reuse, secure, monitor, and serve consistent feature values for training and inference. Teams use it to keep model features consistent between experimentation, batch training, and production inference instead of letting every team rebuild the same transformations differently. It is not a raw data lake, model registry, dashboard catalog, replacement for data governance, or guarantee that features are unbiased, fresh, or useful for every model. Azure Machine Learning intermediate 6 commands Aliases: Azure Machine Learning feature store, managed feature store, ML feature repository, feature registry Quick peek Open full term page

AI and Machine Learning premium Pipeline job In Azure Machine Learning, a pipeline job is a parent job that orchestrates connected command or sweep jobs as steps. It defines inputs, outputs, compute, settings, and step dependencies so reusable training, evaluation, or data-preparation workflows can run repeatably in a workspace. Machine learning intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Prompt flow Prompt flow is a way to build and test an AI application as a flow instead of a single prompt. A flow can connect prompts, Python logic, tools, inputs, outputs, variants, and evaluation steps. In Microsoft Foundry classic and Azure Machine Learning, it helped teams prototype, debug, compare, deploy, and monitor LLM-based applications. In 2026, it is also a migration topic because Microsoft announced that feature development ended and full retirement is planned for April 20, 2027. Azure Machine Learning advanced 18 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Batch deployment A batch deployment in Azure Machine Learning is the deployment configuration behind a batch endpoint, defining the model or pipeline, compute, environment, and execution behavior for asynchronous inference. Azure Machine Learning intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Batch endpoint An Azure Machine Learning batch endpoint is an endpoint for long-running asynchronous inferencing that receives input data references, starts a batch job, and writes outputs for later use. Machine learning advanced 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Inference endpoint Inference endpoint is the Azure concept that controls how applications invoke deployed machine learning models securely, reliably, and measurably in Azure. Teams see it when working with azure machine learning online endpoints, batch endpoints. It is not a model artifact, a training job, an Azure OpenAI deployment, or an application API gateway by itself; that distinction matters because bad assumptions create failed predictions, public exposure. Use the term when reviewing ownership, access, monitoring, cost, recovery, or performance. It keeps architects, operators, security reviewers, and support teams focused on the same resource, setting, or behavior. Azure Machine Learning Intermediate 5 commands Aliases: ML inference endpoint, online endpoint, managed online endpoint, batch endpoint, scoring endpoint Quick peek Open full term page

AI and Machine Learning premium AutoML AutoML is Azure Machine Learning’s way to automate much of the model-search process. In plain terms, you tell Azure what problem you are solving, provide labeled data, choose a metric, and set limits. The service tries different model pipelines and reports which candidates performed best. It helps teams move faster when they need a. Azure Machine Learning intermediate 4 commands Aliases: automated ML, Azure Machine Learning AutoML, automated machine learning, AutoML job Quick peek Open full term page

AI and Machine Learning premium Batch inference Batch inference is the process of generating predictions over larger sets of input data asynchronously, often using Azure Machine Learning batch endpoints and deployments. Azure OpenAI fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

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 premium Endpoint traffic split Azure Machine Learning online endpoints can allocate percentages of live traffic across deployments and can mirror traffic to validate a new deployment before full rollout. Azure Machine Learning intermediate 4 commands Aliases: online endpoint traffic split, traffic allocation, blue green endpoint split Quick peek Open full term page

AI and Machine Learning premium Hyperparameter sweep Hyperparameter sweep is an Azure Machine Learning job pattern that runs multiple training trials across a defined hyperparameter search space. Azure Machine Learning intermediate 4 commands Aliases: Azure ML hyperparameter tuning, hyperparameter sweep, Hyperparameter sweep, Hyperparameter tuning sweep, Sweep job Quick peek Open full term page

AI and Machine Learning premium ML batch endpoint ML batch endpoint is an Azure Machine Learning endpoint designed to run batch scoring jobs against larger datasets instead of serving real-time requests. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML batch endpoint, Azure ML batch endpoint, batch inference endpoint, batch scoring endpoint Quick peek Open full term page

AI and Machine Learning premium ML component ML component is a reusable Azure Machine Learning step definition that packages code, inputs, outputs, environment, and command behavior for pipelines. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML component, Azure ML component, machine learning component, pipeline component Quick peek Open full term page

AI and Machine Learning premium ML compute cluster ML compute cluster is a managed Azure Machine Learning compute target that scales virtual machine nodes for jobs, pipelines, and batch inference workloads. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML compute cluster, AmlCompute, Azure ML compute cluster, managed ML cluster Quick peek Open full term page

AI and Machine Learning premium ML compute instance ML compute instance is a managed Azure Machine Learning development workstation used for notebooks, experimentation, debugging, and interactive data science work. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML compute instance, Azure ML compute instance, ML development VM, cloud workstation Quick peek Open full term page

AI and Machine Learning premium ML data asset ML data asset is a named and versioned Azure Machine Learning reference to data used by jobs, pipelines, training, evaluation, or batch scoring. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML data asset, Azure ML data asset, MLTable asset, data asset, registered data asset Quick peek Open full term page

AI and Machine Learning premium ML datastore ML datastore is a workspace-level Azure Machine Learning connection to underlying storage used by jobs, data assets, pipelines, and experiments. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML datastore, Azure ML datastore, Machine Learning datastore, workspace datastore Quick peek Open full term page

AI and Machine Learning premium ML environment ML environment is a versioned Azure Machine Learning runtime definition that packages software dependencies, base image, and execution settings for jobs or deployments. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML environment, Azure ML environment, ML runtime environment, Machine Learning environment, runtime environment Quick peek Open full term page

AI and Machine Learning premium ML experiment ML experiment is a grouping for related Azure Machine Learning runs or jobs so teams can compare metrics, parameters, artifacts, and outcomes. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Machine Learning intermediate 4 commands Aliases: AML experiment, Azure ML experiment, MLflow experiment, experiment tracking 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 premium Compute cluster a managed Azure Machine Learning pool of CPU or GPU nodes that runs training, batch inference, and other jobs for a workspace Machine learning Intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Compute instance a managed Azure Machine Learning cloud workstation used by one data scientist or engineer for notebooks, experimentation, development, and lightweight testing Azure Machine Learning Beginner 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Responsible AI Microsoft Learn describes Responsible AI as an approach to developing, assessing, and deploying AI systems safely, ethically, and with trust. In Azure Machine Learning, it connects fairness, reliability, privacy, security, transparency, accountability, and operational tooling so teams can evaluate models before production use. Responsible AI fundamentals 8 commands Aliases: Responsible Artificial Intelligence, RAI, trustworthy AI, AI governance, responsible machine learning Quick peek Open full term page

AI and Machine Learning verified Responsible AI dashboard Microsoft Learn describes the Responsible AI dashboard as a single interface for putting Responsible AI into practice in Azure Machine Learning. It brings together tools for model debugging, cohort analysis, interpretability, counterfactuals, causal insights, error analysis, and shareable scorecards for registered models. Azure Machine Learning intermediate 7 commands Aliases: RAI dashboard, Responsible AI insights, model debugging dashboard, Responsible AI scorecard, fairness dashboard Quick peek Open full term page

AI and Machine Learning verified Online deployment An online deployment is the Azure Machine Learning serving configuration behind an online endpoint. It defines the model, scoring code, environment, instance type, instance count, and request settings used for real-time inference, while the endpoint controls authentication, scoring URI, traffic routing, and invocation. Azure Machine Learning intermediate 6 commands Aliases: Azure ML online deployment, managed online deployment, blue deployment, green deployment, online deployment, model-serving deployment Quick peek Open full term page

AI and Machine Learning verified Online endpoint An online endpoint in Azure Machine Learning is an HTTPS endpoint for real-time inference. It exposes deployed models to synchronous clients, defines endpoint name, region, authentication mode, scoring URI, and traffic allocation, and can host one or more online deployments. Azure Machine Learning fundamentals 6 commands Aliases: Azure ML online endpoint, managed online endpoint, real-time inference endpoint, scoring URI, online endpoint Quick peek Open full term page

AI and Machine Learning verified Prompt evaluation Prompt evaluation is how teams test whether a prompt actually works instead of relying on a good demo. You run the AI behavior against a dataset of representative inputs, expected answers, safety cases, or business rules. Evaluators score quality, grounding, safety, formatting, and task success. In Azure and Microsoft Foundry, prompt evaluation helps decide whether a prompt, model, agent, or retrieval change is ready for production. It turns subjective “looks good” reviews into measurable evidence. Azure Machine Learning intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-complete Training job In Azure Machine Learning, a training job executes model-training code on a selected compute target using a defined environment, data, inputs, outputs, and command or pipeline configuration. The service records status, logs, metrics, artifacts, and metadata so experiments can be monitored, reproduced, and promoted. Azure Machine Learning intermediate 6 commands Aliases: Azure ML training job, Azure Machine Learning job, ML training run, command job, model training job Quick peek Open full term page

AI and Machine Learning template-specs-upgraded Run Microsoft Learn describes an Azure ML job as an execution of a task against a compute target, including command, sweep, and pipeline job types. In day-to-day usage, a run is the tracked execution record with status, logs, inputs, outputs, metrics, and artifacts. Azure Machine Learning intermediate 6 commands Aliases: Azure ML run, machine learning run, training run, job run, ML experiment run Quick peek Open full term page

AI and Machine Learning verified Realtime endpoint In Azure Machine Learning, a realtime endpoint is an online endpoint that exposes a model for real-time inferencing over HTTPS. It receives input data, routes traffic to one or more deployments, and returns predictions or other model outputs for interactive applications. Azure Machine Learning intermediate 5 commands Aliases: online endpoint, real-time inference endpoint, managed online endpoint, Azure ML realtime endpoint 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

Analytics field-manual-complete Stream Analytics function A Stream Analytics function is custom logic that can be invoked from a Stream Analytics query. Functions extend the SQL-like language for operations such as specialized calculations, string handling, enrichment, aggregation, or machine-learning scoring when built-in query expressions are not enough. Stream Analytics intermediate 5 commands Aliases: Stream Analytics function, ASA function, Stream Analytics user-defined function, JavaScript UDF for Stream Analytics Quick peek Open full term page

AI and Machine Learning premium field-manual Managed online deployment A managed online deployment is a named deployment behind a managed online endpoint that hosts a model or scoring application for real-time inference. Teams use it when an ML team needs controlled model rollout, traffic shifting, and observable inference behavior. In plain English, it gives operators a named control for safe model release, version isolation, traffic control, and measurable serving health instead of leaving the decision hidden in a portal setting, script, or deployment file. Treat it as production-ready only when the owner, dependencies, permission boundary, monitoring signal, and rollback evidence are clear. Azure Machine Learning intermediate 4 commands Aliases: Managed online deployment, Azure Machine Learning, managed online endpoint, batch endpoint, model deployment, online deployment, Azure ML managed deployment, Azure Machine Learning managed online endpoints Quick peek Open full term page

AI and Machine Learning premium field-manual Managed online endpoint A managed online endpoint is an Azure ML endpoint that provides a managed HTTPS interface for real-time model inference. Teams use it when applications need a stable scoring URL while deployments behind it can change safely. In plain English, it gives operators a named control for centralized inference access, traffic routing, authentication, monitoring, and deployment isolation instead of leaving the decision hidden in a portal setting, script, or deployment file. Treat it as production-ready only when the owner, dependencies, permission boundary, monitoring signal, and rollback evidence are clear. Machine learning intermediate 4 commands Aliases: Managed online endpoint, Azure Machine Learning, Machine learning, managed online deployment, batch endpoint, inference endpoint, Azure ML managed endpoint, real-time inference endpoint, Azure Machine Learning online inference Quick peek Open full term page

AI and Machine Learning field-manual-ready ML job An ML job is a submitted unit of work in Azure Machine Learning, such as training, evaluation, batch scoring, or a pipeline step. Azure Machine Learning intermediate 4 commands Aliases: AML job, Azure ML job, command job, machine learning job Quick peek Open full term page