Official Technical Resources: Microsoft (page 10)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 136–150.
Lag features for time-series forecasting in AutoML
Explore how automated machine learning (AutoML) in Azure Machine Learning creates lag and rolling window aggregation to forecast time-series regression models.
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
Overview of forecasting methods in AutoML
Learn how AutoML in Azure Machine Learning uses machine learning to build forecasting models, including time series or regression models for predictions.
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
Model sweeping and selection for forecasting in AutoML
Explore how you can use automated machine learning (AutoML) in Azure Machine Learning to search for (sweep) and select forecasting models.
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
Make data-driven policies and influence decision-making
Make data-driven decisions and policies with the Responsible AI dashboard's integration of the causal analysis tool EconML.
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: AI Interpretability · Supervised Learning
Link last checked: 2026-09-24
Counterfactuals analysis and what-if
Generate diverse counterfactual examples with feature perturbations to see minimal changes required to achieve desired prediction with the Responsible AI dashboard's integration of DiCE 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: AI Interpretability · Supervised Learning
Link last checked: 2026-09-24
Model performance and fairness
Learn about machine learning fairness and how the Fairlearn Python package can help you assess and mitigate unfairness.
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: AI Governance and Regulation · Bias and Fairness · AI Interpretability
Link last checked: 2026-09-24
Machine learning registries for MLOps
Learn about Azure Machine Learning registries and their role in scaling MLOps across different environments.
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: AI Agent · Agent Frameworks
Link last checked: 2026-09-24
Azure Machine Learning model monitoring
Learn about monitoring the performance of models deployed to production on Azure Machine Learning, including lookback windows, monitoring signals, and metrics.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
What is Responsible AI?
Learn what Responsible AI is and how to use it with Azure Machine Learning to understand models, protect data, and control the model lifecycle.
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: AI Governance and Regulation · Bias and Fairness · AI Interpretability
Link last checked: 2026-09-24
Assess AI systems by using the Responsible AI dashboard
Learn how to use the comprehensive UI and SDK/YAML components in the Responsible AI dashboard to debug your machine learning models and make data-driven decisions.
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: AI Governance and Regulation · Bias and Fairness · AI Interpretability
Link last checked: 2026-09-24
Share Responsible AI insights using the Responsible AI scorecard (preview)
Learn about how to use the Responsible AI scorecard to share responsible AI insights from your machine learning models and make data-driven decisions with nontechnical and technical stakeholders.
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: AI Governance and Regulation · Bias and Fairness · AI Interpretability
Link last checked: 2026-09-24
Make predictions with ONNX on computer vision models from AutoML
Use ONNX with Azure Machine Learning automated ML to make predictions on computer vision models for classification, object detection, and instance segmentation.
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
Model interpretability
Learn how your machine learning model makes predictions during training and inferencing by using the Azure Machine Learning CLI and Python SDK.
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: AI Interpretability · Supervised Learning
Link last checked: 2026-09-24
Prepare data for computer vision tasks by using automated machine learning
Learn about image data preparation for Azure Machine Learning to train computer vision models on classification, object detection, and segmentation.
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
Hyperparameter tuning a model (v2)
Discover how to define search spaces, choose sampling algorithms, and configure early termination policies for hyperparameter tuning in Azure ML.
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 · Regularization
Link last checked: 2026-09-24