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Glossary

Concise, technically precise definitions of the terms used across the course. Where useful, each entry notes why the concept matters in practice.

Azure ML platform

  • Workspace : the top-level Azure ML container and governance boundary that ties together compute, data, models, and endpoints under one identity and access policy.
  • Control plane : the metadata/intent layer (asset registration, run history, RBAC, lineage).
  • Data plane : the execution layer (compute, data movement, inference) where cost and performance are determined.
  • Compute Instance : a single always-on VM for interactive development and debugging.
  • Compute Cluster : auto-scaling compute (0?N nodes) for training, sweeps, and AutoML trials; scales back to zero when idle.
  • Environment : a versioned, pinned runtime (base image + dependencies) reused across training and inference to eliminate training/serving skew.
  • Datastore : a registered connection to underlying storage (e.g. blob, data lake).
  • Data asset / dataset : a versioned pointer to a specific data snapshot used by jobs.
  • Model registry : versioned store of trained model artifacts with lineage back to the run, data, and environment that produced them.
  • Endpoint : the deployed, addressable serving surface for a model (online or batch).
  • Lineage : the recorded chain data version ? run ? model version ? endpoint revision that makes predictions reproducible and auditable.
  • Managed identity : an Azure-managed credential attached to a workload so jobs access resources without embedded secrets.
  • RBAC : role-based access control; permissions granted to identities via roles, applied with least privilege.

Core ML concepts

  • Supervised / unsupervised / reinforcement learning : learning from labeled data, from unlabeled structure, and from environment reward respectively.
  • Self-supervised learning : manufacturing a supervised signal from the data itself (predict the masked token); the basis of foundation models.
  • Feature : an input variable; the feature vector \(x \in \mathbb{R}^d\) describes one example.
  • Label / target : the value a supervised model predicts.
  • Parameter : a value learned during training (a weight). Hyperparameter : a value set before training (e.g. learning rate, tree depth), tuned on validation data.
  • Loss function : scores how wrong a prediction is; training minimizes its average (empirical risk minimization).
  • Gradient descent : iterative parameter update \(\theta \leftarrow \theta - \eta\nabla\mathcal{L}\); \(\eta\) is the learning rate (step size).
  • Regularization : a penalty discouraging complexity; L1 induces sparsity (feature selection), L2 shrinks weights for stability.
  • Bias / variance : error from over-simplicity (underfitting) vs error from over-sensitivity to the training sample (overfitting).
  • Overfitting : low training error but high test error; the model memorized noise.
  • Cross-validation : rotating train/validation folds to estimate generalization with lower variance.
  • Data leakage : information unavailable at prediction time entering training, inflating offline metrics.

Modeling and evaluation

  • Featurization : transforming raw fields into model-ready features.
  • Logistic regression / sigmoid : linear model with a sigmoid squashing output into a probability.
  • Decision tree / random forest / gradient boosting : tree splitting by purity; bagging trees (forest) reduces variance; boosting trees sequentially reduces bias.
  • Ensemble (bagging / boosting / stacking) : combining models to reduce variance, reduce bias, or learn an optimal combination.
  • Threshold : the probability cutoff converting scores to decisions; tuned by error costs.
  • Precision / recall / F1 : correctness of positives / coverage of positives / their harmonic mean.
  • ROC-AUC / PR-AUC : threshold-free ranking quality overall / focused on the positive class.
  • MAE / RMSE / \(R^2\) : average absolute error / outlier-sensitive error / fit vs the mean baseline.
  • Calibration : agreement between predicted probabilities and observed frequencies.
  • SHAP / LIME / permutation importance : game-theoretic attribution / local surrogate explanation / shuffle-based global importance.

Operations and MLOps

  • Drift : change over time in inputs (covariate drift, \(P(X)\)) or in the input?target relationship (concept drift, \(P(Y\mid X)\)).
  • PSI : Population Stability Index; a single number measuring how far a distribution moved from baseline.
  • Endpoint (online vs batch) : real-time request/response vs scheduled bulk scoring.
  • Blue/green, canary, shadow : release strategies trading off rollback speed and exposure of a new model version.
  • SLI / SLO : a measured reliability indicator / its target threshold.
  • Cold start : one-time latency of loading the model when a serving replica starts.
  • CrashLoopBackOff : Kubernetes state where a container repeatedly starts and exits; for ML, usually a failed model load in init().
  • Readiness / liveness probe : checks that gate traffic to a pod / restart a stuck pod.
  • Model card : a document recording a model's intended use, data, metrics, fairness, limits, and operations.