Install
Data Science & ML Platforms terms
Notebooks, MLOps, feature stores and the platforms models are built on.
AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. Classification A supervised learning task that assigns each input to one of a fixed set of discrete categories or labels. Clustering An unsupervised technique that groups data points so items within a group are more similar to each other than to items outside it. Concept drift A change over time in the underlying relationship between a model's inputs and the outcome it predicts. Confusion matrix A table comparing a classifier's predicted labels against actual labels, breaking results into true and false positives and negatives. Data drift A change in the statistical properties of input data over time that can silently degrade a deployed model's accuracy. Decision tree A supervised model that predicts an outcome by following a sequence of if-then splits on feature values, shaped like a tree. Deep learning Machine learning using neural networks with many layers, able to learn complex patterns directly from raw data such as images or text. Dimensionality reduction Techniques that compress a dataset with many variables into fewer dimensions while preserving as much meaningful information as possible. Explainable AI (XAI) Techniques and models that make a machine learning system's predictions understandable to humans, rather than a black box. F1 score The harmonic mean of precision and recall, giving a single score that balances both when neither error type is clearly more costly. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Feature store A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions. Fine-tuning Further training a pretrained model on a smaller, targeted dataset to adapt its behavior to a specific task or domain. Gradient boosting An ensemble method that builds trees one at a time, each one correcting the errors made by the trees before it. Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns. K-means clustering A clustering algorithm that partitions data into k groups by repeatedly assigning points to the nearest of k cluster centers. Knowledge graph A network of entities and the labeled relationships between them, used to represent and query structured knowledge. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Model monitoring Ongoing tracking of a deployed model's inputs, predictions and outcomes to catch accuracy loss, drift or failures after launch. Neural network A model made of layers of connected nodes that learns to map inputs to outputs by adjusting connection weights during training. Overfitting When a model learns the noise and specific quirks of its training data so closely that it performs poorly on new data. Precision and recall Two complementary classifier metrics; precision is how many predicted positives were correct, recall is how many actual positives were found. Random forest An ensemble model that trains many decision trees on random subsets of data and features, then averages their predictions. Reinforcement learning A learning approach where an agent learns a strategy by taking actions in an environment and receiving rewards or penalties over time. Retrieval-augmented generation (RAG) An architecture that retrieves relevant documents at query time and feeds them to a language model so its answers are grounded in sources. ROC AUC (area under the ROC curve) A single number summarizing how well a classifier separates positive from negative cases across every possible decision threshold. SHAP values (SHapley Additive exPlanations) A method for explaining an individual prediction by fairly attributing it to the contribution of each input feature. Supervised learning A machine learning approach where a model learns from examples that already include the correct answer, to predict that answer on new data. Train-test split Dividing a dataset into separate portions for training a model and for evaluating it, so performance is measured on unseen data. Training data The dataset with known outcomes that a machine learning model learns patterns from before being used to predict new cases. Unsupervised learning A machine learning approach that finds structure or patterns in data that has no labeled correct answer to learn from.