AWS Certified Machine Learning Engineer - Associate: key terms

Flashcards for this exam AWS Certified Machine Learning Engineer - Associate guide Class dates

• AccuracyThe fraction of all predictions that are correct: (TP + TN) / total.Foundational
• Amazon BedrockAWS's fully managed service for building generative AI applications with foundation models through a single API.Foundational
• Amazon SageMaker AIAWS's fully managed service to build, train, tune, deploy and monitor machine learning models.Foundational
• Amazon SageMaker CanvasA no-code SageMaker AI interface for preparing data, building ML models and using generative AI.Intermediate
• Amazon SageMaker ClarifyA SageMaker AI capability for detecting bias in data and models and explaining predictions.Intermediate
• Amazon SageMaker Ground TruthAWS service for creating labeled training datasets using human workforces and automated labeling.Intermediate
• Amazon SageMaker JumpStartA SageMaker AI hub of pretrained and foundation models and solutions that can be deployed and fine-tuned in your account.Intermediate
• Anomaly DetectionIdentifying data points, events or patterns that deviate significantly from expected behavior.Intermediate
• Area Under the Curve (AUC)The area under the ROC curve, summarizing a classifier's ability to rank positives above negatives (0.5 = random, 1.0 = perfect).Intermediate
• AutoMLAutomated machine learning: tools that automate algorithm selection, feature preprocessing and hyperparameter tuning.Foundational
• AWS AI ServicesPretrained, task-specific AWS APIs such as Rekognition, Comprehend, Textract, Transcribe, Polly, Translate, Lex, Kendra and Personalize.Foundational
• BaggingBootstrap aggregating: training models in parallel on random samples with replacement and averaging their outputs.Intermediate
• Baseline ModelA simple reference model used to judge whether a more complex model adds value.Foundational
• Batch InferenceGenerating predictions for a large dataset offline in a single job rather than per request.Foundational
• Batch SizeThe number of training examples processed together before the model's parameters are updated.Foundational
• Bayesian OptimizationHyperparameter tuning that builds a probabilistic model of performance to choose the most promising next trial.Advanced
• Bias-Variance TradeoffThe balance between error from overly simple assumptions (bias) and error from sensitivity to training data (variance).Intermediate
• Binary ClassificationClassification with exactly two possible output classes, such as yes/no or positive/negative.Foundational
• BoostingAn ensemble method that trains weak learners sequentially, weighting each toward the errors of its predecessors.Intermediate
• Canary DeploymentReleasing a new model to a small fraction of traffic first, then increasing gradually if metrics are healthy.Intermediate
• Class ImbalanceA dataset where one class is far more frequent than others, which can bias models toward the majority class.Intermediate
• ClassificationA supervised learning task that predicts which discrete category or class an input belongs to.Foundational
• Classification ThresholdThe probability cutoff above which a classifier predicts the positive class.Intermediate
• ClusteringAn unsupervised task that groups similar data points together so items in a cluster are more alike than items in other clusters.Foundational
• Collaborative FilteringRecommending items based on the preferences of similar users or similarity between items in interaction data.Intermediate
• Concept DriftA change in the relationship between inputs and the target variable over time.Intermediate
• Confusion MatrixA table comparing predicted classes with actual classes, showing true/false positives and negatives.Foundational
• Continuous Training (CT)Automatically retraining and redeploying models on new data on a schedule or when drift is detected.Intermediate
• Cross-Entropy LossA loss that measures the difference between predicted class probabilities and the true labels; standard for classification and LLMs.Intermediate
• Cross-ValidationA resampling method that trains and evaluates a model on multiple different splits to estimate generalization more reliably.Intermediate
• Curse of DimensionalityThe set of problems that arise as feature count grows: data becomes sparse and distance measures lose meaning.Intermediate
• Data AugmentationExpanding a training set by creating modified copies of existing examples.Intermediate
• Data DriftA change in the statistical distribution of input data in production compared with training data.Intermediate
• Data LabelingThe process of annotating raw data with labels so it can be used for supervised training or evaluation.Foundational
• Data LeakageWhen information unavailable at prediction time leaks into training, producing unrealistically good evaluation results.Intermediate
• Data LineageThe record of where data originated and how it was transformed on its way to a model or report.Intermediate
• Decision TreeA model that predicts by learning a hierarchy of if-then splits on feature values.Foundational
• Dimensionality ReductionTechniques that reduce the number of input features while preserving as much useful information as possible.Intermediate
• Early StoppingHalting training when validation performance stops improving to prevent overfitting.Intermediate
• Ensemble LearningCombining predictions from multiple models to achieve better performance than any single model.Intermediate
• EpochOne complete pass of the training algorithm through the entire training dataset.Foundational
• Experiment TrackingRecording the code, data, parameters, metrics and artifacts of each training run for comparison and reproducibility.Intermediate
• ExplainabilityThe ability to describe why an AI model produced a particular output in terms humans can understand.Foundational
• F1 ScoreThe harmonic mean of precision and recall: 2PR / (P + R).Foundational
• FeatureAn individual measurable input variable that a model uses to make predictions.Foundational
• Feature EngineeringCreating, transforming and selecting input variables to improve model performance.Foundational
• Feature ScalingTransforming numeric features to a common range or distribution so no feature dominates due to its units.Foundational
• Feature SelectionChoosing a subset of the most relevant features and discarding redundant or irrelevant ones.Intermediate
• Feature StoreA centralized repository for storing, sharing and serving ML features consistently for training and inference.Intermediate
• Gradient BoostingAn ensemble technique that builds models sequentially, each one correcting the residual errors of the previous ones.Intermediate
• Gradient DescentAn optimization algorithm that iteratively adjusts parameters in the direction that most reduces the loss.Intermediate
• Grid SearchHyperparameter tuning that exhaustively tries every combination from predefined value lists.Intermediate
• Ground TruthThe verified correct answer or label used to train and evaluate models.Foundational
• Hugging FaceA platform and open-source community hosting models, datasets and libraries such as Transformers.Foundational
• HyperparameterA configuration value set before training that controls the learning process, rather than being learned from data.Foundational
• Hyperparameter TuningSearching for the hyperparameter values that give the best validation performance.Intermediate
• ImputationFilling in missing values in a dataset with estimated substitutes.Foundational
• InferenceUsing a trained model to make predictions or generate outputs on new data.Foundational
• K-Means ClusteringAn unsupervised algorithm that partitions data into k clusters by assigning points to the nearest centroid and iteratively updating centroids.Foundational
• K-Nearest Neighbors (k-NN)A non-parametric algorithm that predicts by majority vote or average of the k closest training examples.Intermediate
• L1 RegularizationAdds a penalty proportional to the absolute value of weights, pushing some weights to exactly zero (Lasso).Intermediate
• L2 RegularizationAdds a penalty proportional to the squared weights, shrinking all weights toward zero (Ridge, weight decay).Intermediate
• LabelThe known target value attached to a training example that a supervised model learns to predict.Foundational
• Learning RateThe hyperparameter that sets how large a step the optimizer takes when updating weights.Foundational
• Linear RegressionA regression algorithm that models the target as a weighted sum of input features plus an intercept.Foundational
• Logistic RegressionA classification algorithm that applies the sigmoid function to a linear combination of features to output a probability.Foundational
• Loss FunctionA function that measures how far a model's predictions are from the true values; training minimizes it.Intermediate
• Machine Learning (ML)A subset of AI in which systems learn patterns from data to make predictions or decisions without being explicitly programmed with rules.Foundational
• Machine Learning Operations (MLOps)Practices and tooling that apply DevOps principles to build, deploy, monitor and maintain ML models reliably in production.Foundational
• Mean Absolute Error (MAE)The average of absolute differences between predicted and actual values.Foundational
• Mean Squared Error (MSE)The average of squared differences between predicted and actual values.Foundational
• ML PipelineAn automated, repeatable sequence of steps such as data preparation, training, evaluation and deployment.Intermediate
• MLflowAn open-source platform for experiment tracking, model packaging, model registry and evaluation.Intermediate
• Model EvaluationMeasuring a trained model's performance, robustness and fitness for purpose before and after deployment.Foundational
• Model MonitoringContinuously tracking a deployed model's inputs, outputs, performance and fairness to detect degradation.Intermediate
• Model ParametersThe internal values, such as weights and biases, that a model learns from training data.Foundational
• Model RegistryA central catalog that stores model versions with metadata, lineage and approval status for deployment.Intermediate
• Model ServingHosting a trained model behind an interface so applications can request predictions.Foundational
• Model TrainingThe process of fitting a model's parameters to data by optimizing an objective function.Foundational
• Multiclass ClassificationClassification where each input is assigned to exactly one of three or more classes.Intermediate
• Naive BayesA probabilistic classifier based on Bayes' theorem that assumes features are independent given the class.Intermediate
• NormalizationMin-max scaling that rescales a feature to a fixed range, usually 0 to 1.Foundational
• One-Hot EncodingRepresenting a categorical variable as multiple binary columns, one per category.Foundational
• OverfittingWhen a model learns training data too closely, including noise, and performs poorly on new data.Foundational
• PolicyIn reinforcement learning, the strategy that maps states to actions.Intermediate
• PrecisionOf all positive predictions, the fraction that are actually positive: TP / (TP + FP).Foundational
• Principal Component Analysis (PCA)A linear technique that projects data onto orthogonal directions of maximum variance to reduce dimensionality.Intermediate
• PyTorchAn open-source deep learning framework, originally from Meta and now governed by the PyTorch Foundation, known for dynamic computation graphs.Foundational
• R-Squared (R²)The coefficient of determination: the proportion of variance in the target explained by the model.Intermediate
• Random ForestAn ensemble of decision trees trained on random data samples and feature subsets, averaging their predictions.Intermediate
• Real-Time InferenceServing predictions synchronously with low latency in response to individual requests.Foundational
• RecallOf all actual positives, the fraction the model correctly identifies: TP / (TP + FN). Also called sensitivity or true positive rate.Foundational
• Recommendation SystemA system that predicts items a user is likely to want, based on behavior, item attributes or both.Intermediate
• RegressionA supervised learning task that predicts a continuous numeric value.Foundational
• RegularizationTechniques that constrain model complexity to reduce overfitting.Intermediate
• Reinforcement Learning (RL)Learning in which an agent takes actions in an environment and improves its policy by maximizing cumulative reward.Foundational
• Reward FunctionThe signal that tells a reinforcement learning agent how good an action or outcome was.Intermediate
• ROC CurveReceiver Operating Characteristic curve: a plot of true positive rate against false positive rate across thresholds.Intermediate
• Root Mean Squared Error (RMSE)The square root of MSE, expressing typical prediction error in the target's own units.Foundational
• Semi-Supervised LearningLearning that combines a small labeled dataset with a large unlabeled dataset to improve model accuracy.Intermediate
• Shadow DeploymentRunning a new model alongside production on live traffic without returning its predictions to users.Intermediate
• SHAPSHapley Additive exPlanations: a method that attributes a prediction to each feature using game-theoretic Shapley values.Intermediate
• StandardizationScaling a feature to have mean 0 and standard deviation 1 (z-score scaling).Foundational
• Stochastic Gradient Descent (SGD)Gradient descent that updates parameters using one example or a small mini-batch at a time.Intermediate
• Supervised LearningMachine learning that trains a model on labeled examples, where each input is paired with the correct output.Foundational
• Support Vector Machine (SVM)A supervised algorithm that finds the hyperplane separating classes with the maximum margin.Intermediate
• Test DatasetData kept completely separate until the end to measure a final model's generalization performance.Foundational
• Time Series ForecastingPredicting future values of a variable from its historical, time-ordered observations.Intermediate
• Train-Test SplitDividing a dataset into separate subsets for training and evaluation.Foundational
• Training DatasetThe portion of data used to fit a model's parameters.Foundational
• Transfer LearningReusing a model trained on one task as the starting point for a related task.Intermediate
• UnderfittingWhen a model is too simple to capture underlying patterns and performs poorly on both training and new data.Foundational
• Unsupervised LearningMachine learning that finds structure, groupings or patterns in data that has no labels.Foundational
• Validation DatasetHeld-out data used during development to tune hyperparameters and select models.Foundational
• XGBoostExtreme Gradient Boosting: a fast, regularized, widely used gradient-boosted decision tree library.Intermediate