Glossary¶

The DataRobot glossary provides brief definitions of terms relevant to the DataRobot platform. These terms span all phases of machine learning, from data to deployment.

All Data Modeling Time-aware Predictions MLOps Generative AI Code-first

A¶

Accuracy over space¶

A model Leaderboard tab (Evaluate > Accuracy Over Space) and Location AI insight that provides a spatial residual mapping within an individual model.

Accuracy over time¶

A model Leaderboard tab (Evaluate > Accuracy Over Time) that visualizes how predictions change over time.

ACE scores¶

Also known as Alternating Conditional Expectations. A univariate measure of correlation between the feature and the target. ACE scores detect non-linear relationships, but as they are univariate, they do not detect interaction effects.

Actuals¶

Actual values for an ML model that let you track its prediction outcomes. To generate accuracy statistics for a deployed model, you compare the model's predictions to real-world actual values for the problem. Both the prediction dataset and the actuals dataset must contain association IDs, which let you match up corresponding rows in the datasets to gauge the model's accuracy.

Advanced tuning¶

The ability to manually set model parameters after the model build, supporting experimentation with parameter settings to improve model performance.

Aggregate image feature¶

Used with Visual AI, a set of image features where each individual element of that set is a constituent image feature. For example, the set of image features extracted from an image might include a set of features indicating:

1. The colors of the individual pixels in the image.
2. Where edges are present in the image.
3. Where faces are present in the image.

From the aggregate it may be possible to determine the impact of that feature on the output of a data analytics model and compare that impact to the impacts of the model's other features.

AI Catalog¶

A browsable and searchable collection of registered objects that contains definitions and relationships between various objects types. Items stored in the catalog include: data connections, data sources, data metadata.

AIM¶

The second phase of Exploratory Data Analysis (i.e., EDA2), that determines feature importance based on cross-correlation with the target feature. That data determines the "informative features" used for modeling during Autopilot.

Alternating conditional expectations¶

See ACE scores.

Anomaly detection¶

A form of unsupervised learning used to detect anomalies in data. Anomaly detection, also referred to as outlier or novelty detection, can be useful with data having a low percentage of irregularities or large amounts of unlabeled data. See also unsupervised learning.

Apps¶

See No-Code AI Apps.

ARIMA (AutoRegressive Integrated Moving Average)¶

A class of time series model that projects the future values of a series based entirely on the patterns of that series.

Asset¶

One of the components of a Use Case that can be added, managed, and shared within Workbench. Components include data, vector databases, experiments, playgrounds, apps, and notebooks.

Association ID¶

An identifier that functions as a foreign key for your prediction dataset so you can later match up actual values (or "actuals") with the predicted values from the deployed model. An association ID is required for monitoring the accuracy of a deployed model.

AUC (Area Under the Curve)¶

A common error metric for binary classification that considers all possible thresholds and summarizes performance in a single value on the ROC Curve. It works by optimizing the ability of a model to separate the 1s from the 0s. The larger the area under the curve, the more accurate the model.

Audit log¶

A chronological, immutable record of all significant activities performed within the DataRobot platform by users and automated processes. It is essential for security audits, compliance reporting, and troubleshooting. Sometimes referred to as an "event log".

Augmented intelligence¶

DataRobot's enhanced approach to artificial intelligence, which expands current model building and deployment assistance practices. The DataRobot platform fully automates and governs the AI lifecycle from data ingest to model training and predictions to model-agnostic monitoring and governance. Guardrails ensure adherence to data science best practices when creating machine learning models and AI applications. Transparency across user personas and access to data wherever it resides avoids lock-in practices.

Authentication¶

The process of verifying the identity of a user or system before granting access to an API or service, commonly using API keys, OAuth tokens, or other credentials.

Authorization¶

The process of determining what actions or resources a user or system is permitted to access after authentication.

Automated retraining¶

Retraining strategies for MLOps that refresh production models based on a schedule or in response to an event (for example, a drop in accuracy or data drift). Automated Retraining also uses DataRobot's AutoML create and recommend new challenger models. When combined, these strategies maximize accuracy and enable timely predictions.

AutoML (Automated Machine Learning)¶

A software system that automates many of the tasks involved in preparing a dataset for modeling and performing a model selection process to determine the performance of each with the goal of identifying the best performing model for a specific use case. Used for predictive modeling; see also time series for forecasting.

Autopilot (full Autopilot)¶

The DataRobot "survival of the fittest" modeling mode that automatically selects the best predictive models for the specified target feature and runs them at ever-increasing sample sizes. In other words, it runs more models in the early stages on a small sample size and advances only the top models to the next stage. In full Autopilot, DataRobot runs models at 16% (by default) of total data and advances the top 16 models, then runs those at 32%. Taking the top 8 models from that run, DataRobot runs on 64% of the data (or 500MB of data, whichever is smaller). See also Quick (Autopilot), Comprehensive, and Manual.

AutoTS (Automated time series)¶

A software system that automates all or most of the steps needed to build forecasting models, including featurization, model specification, model training, model selection, validation, and forecast generation. See also time series.

Average baseline¶

The average of the target in the Feature Derivation Window; used in time series modeling.

B¶

Backend¶

The server-side components of LLM and AI applications that handle data processing, model inference, business logic, and database operations.

Backtesting¶

The time-aware equivalent of cross-validation. Unlike cross-validation, however, backtests allow you to select specific time periods or durations for your testing instead of random rows, creating "trials" for your data.

Baseline model¶

Also known as a naive model. A simple model used as a comparison point to confirm that a generated ML or time series model is learning with more accuracy than a basic non-ML model.

For example, generated ML models for a regression project should perform better than a baseline model that predicts the mean or median of the target. Generated ML models for a time series project should perform better than a baseline model that predicts the future using the most recent actuals (i.e., using today's actual value as tomorrow's prediction).

For time series projects, baseline models are used to calculate the MASE metric (the ratio of the MAE metric over the baseline model).

Batch predictions¶

A method of making predictions with large datasets, in which you pass input data and get predictions for each row; predictions are written to output files. Users can make batch predictions with MLOps via the Predictions interface or can use the Batch Prediction API for automating predictions. Schedule batch prediction jobs by specifying the prediction data source and destination and determining when the predictions will be run.

Bias mitigation¶

Augments blueprints with a pre- or post-processing task intended to reduce bias across classes in a protected feature. Bias Mitigation is also a model Leadboard tab (Bias and Fairness > Bias Mitigation) where you can apply mitigation techniques after Autopilot has finished.

Bias vs accuracy¶

A Leaderboard tab that generates a chart to show the tradeoff between predictive accuracy and fairness, removing the need to manually note each model's accuracy score and fairness score for the protected features.

Blind history¶

"Blind history", used in time-aware modeling, captures the gap created by the delay of access to recent data (e.g., "most recent" may always be one week old). It is defined as the period of time between the smaller of the values supplied in the Feature Derivation Window and the forecast point. A gap of zero means "use data up to, and including, today;" a gap of one means "use data starting from yesterday" and so on.

Blender¶

A model that potentially increases accuracy by combining the predictions of between two and eight models. DataRobot can be configured to automatically create blender models as part of Autopilot, based on the top three regular Leaderboard models (for AVG, GLM, and ENET blenders). You can also create blenders manually (aka ensemble models).

Blueprint¶

A blueprint is a graphical representation of the many steps involved in transforming input predictors and targets into a model. It represents the high-level end-to-end procedure for fitting the model, including any preprocessing steps, algorithms, and post-processing. Each box in a blueprint may represent multiple steps. You can view the graphical representation of a blueprint by clicking on a model on the Leaderboard. See also user blueprints.

C¶

Caching strategies¶

Techniques for storing frequently accessed LLM responses, embeddings, or intermediate results to improve performance and reduce computational costs.

Canary deployment¶

A deployment strategy for LLM and AI models that gradually rolls out new versions to a small subset of users before full deployment, allowing for early detection of issues.

"Can't operationalize" period¶

The "can't operationalize" period, used in time series modeling, defines the gap of time immediately after the Forecast Point and extending to the beginning of the Forecast Window. It represents the time required for a model to be trained, deployed to production, and to start making predictions—the period of time that is too near-term to be useful. For example, predicting staffing needs for tomorrow may be too late to allow for taking action on that prediction.

Catalog¶

See AI Catalog.

Centroid¶

The center of a cluster generated using unsupervised learning. A centroid is the multi-dimensional average of a cluster, where the dimensions are observations (data points).

CFDS (Customer Facing Data Scientist)¶

A DataRobot employee responsible for the technical success of user and potential users. They assist with tasks like structuring data science problems to complete integration of DataRobot. CFDS are passionate about ensuring user success.

Chain-of-thought¶

A prompting technique that encourages a language model to reason step-by-step, improving its ability to solve complex problems or explain its answers.

Challenger models¶

Models that you can compare to a currently deployed model (the "champion" model) to continue model comparison post-deployment. Submit a challenger model to shadow a deployed model and replay predictions made against the champion to determine if there is a superior DataRobot model that would be a better fit.

Champion model¶

A model recommended by DataRobot—for a deployment (predictions) or for time series segmented modeling.

In MLOps, you can replace the champion selected for a deployment yourself, or you can set up Automated Retraining, where DataRobot compares challenger models with the champion model and replaces the champion model if a challenger outperforms the champion.

In the segmented modeling workflow, DataRobot builds a model for each segment. DataRobot recommends the best model for each segment—the segment champion. The segment champions roll up into a Combined Model. For each segment, you can select a different model as champion, which is then used in the Combined Model.

Channel¶

The connection between an output port of one module and an input port of another module. Data flows from one module's output port to another module's input port via a channel, represented visually by a line connecting the two.

Chatting¶

Sending prompts (and as a result, LLM payloads) to LLM endpoints based on a single LLM blueprint and receiving a response from the LLM. In this case, context from previous prompts/responses is sent along with the payload.

Chunking¶

The action of taking a body of unstructured text and breaking it up into smaller pieces of unstructured text (tokens).

Citation¶

The chunks of text from the vector database used during the generation of LLM responses.

CI/CD pipelines¶

Continuous Integration (CI) and Continuous Deployment (CD) pipelines that automate the building, testing, and deployment of LLM and AI applications to ensure reliable and consistent releases.

Circuit breaker¶

A design pattern that prevents cascading failures in LLM and AI systems by temporarily stopping requests to failing services, allowing them to recover.

Classification¶

A type of prediction problem that classifies values into discrete, final outcomes or classes. Binary classification problems are those datasets in which what you are trying to predict can be one of two classes (for example, "yes" or "no"). Multiclass classification is a classification problem that results in more than two outcomes (for example, "buy", "sell", or "hold"). Unlimited multiclass is the ability to handle projects with a target feature containing an unlimited number of classes, with support for both a high threshold of individual classes and multiclass aggregation to support an unlimited number of classes above the threshold. See also regression.

Clustering¶

A form of unsupervised learning used to group similar data and identify natural segments.

Codespace¶

A fully configured Integrated Development Environment (IDE) hosted on the cloud. It provides tools for you to write, test, and debug code. It also offers file storage so that notebooks inside a codespace can reference Python utility scripts and other assets.

Coefficients¶

A model Leaderboard tab (Describe > Coefficients) that provides a visual indicator of information that can help you refine and optimize your models.

Combined model¶

The final model generated in a time series segmented modeling workflow. With segmented modeling, DataRobot builds a model for each segment and combines the segment champions into a single Combined Model that you can deploy.

Common event¶

A data point is a common event if it occurs in a majority of weeks in data (for example, regular business days and hours would be common, but an occasional weekend data point would be uncommon).

Compliance documentation¶

Automated model development documentation that can be used for regulatory validation. The documentation provides comprehensive guidance on what constitutes effective model risk management.

Compliance reporting¶

The generation of reports and documentation required for regulatory compliance in LLM and AI deployments, including data usage, model performance, and security measures.

Composable ML¶

A code-centric feature, designed for data scientists, that allows applying custom preprocessing and modeling methods to create a blueprint for model training. Using built-in and custom tasks, you can compose and then integrate the new blueprint with other DataRobot features to augment and improve machine learning pipelines.

Comprehensive¶

A modeling mode that runs all Repository blueprints on the maximum Autopilot sample size to ensure more accuracy for models.

Computer vision¶

Use of computer systems to analyze and interpret image data, used with Visual AI. Computer vision tools generally use models that incorporate principles of geometry to solve specific problems within the computer vision domain. For example, computer vision models may be trained to perform object recognition (recognizing instances of objects or object classes in images), identification (identifying an individual instance of an object in an image), detection (detecting specific types of objects or events in images), etc.

Computer vision tools/techniques¶

Tools—for example, models, systems—that perform image preprocessing, feature extraction, and detection/segmentation functions.

Configuration management¶

The practice of managing LLM and AI system configurations across different environments (development, staging, production) to ensure consistency and reduce deployment errors.

Confusion matrix¶

A table that reports true versus predicted values. The name "confusion matrix" refers to the fact that the matrix makes it easy to see if the model is confusing two classes (consistently mislabeling one class as another class). The confusion matrix is available as part of the ROC Curve, Eureqa, and Confusion Matrix for multiclass model visualizations in DataRobot.

Connection instance¶

A connection that is configured with metadata about how to connect to a source system (e.g., instance of a Snowflake connection).

Console¶

Console is a central hub for deployment management activity. Its dashboard provides access to deployed models for further monitoring and mitigation. It also provides access to prediction activities and allows you to view, create, edit, delete, or share serverless and external prediction environments

Constraints¶

A model Leaderboard tab (Describe > Constraints) that allows you to review monotonically constrained features if feature constraints were configured in Advanced Options prior to modeling.

Container orchestration¶

The automated management of containerized LLM and AI applications, including deployment, scaling, networking, and availability, typically using platforms like Kubernetes.

Context window¶

The maximum amount of text (measured in tokens) that a large language model can process at one time. The context window determines how much prior conversation or document content the model can "see" when generating a response.

Cost allocation¶

The process of assigning LLM and AI service costs to different teams, projects, or business units for budgeting and chargeback purposes.

Credentials¶

Information used to authenticate and authorize actions against data connections. The most common connection is through username and password, but alternate authentication methods include LDAP, Active Directory, and Kerberos.

Cross-class accuracy¶

A model Leaderboard tab (Bias and Fairness > Cross-Class Accuracy) that helps to shows why the model is biased, and where in the training data it learned the bias from. Bias and Fairness settings must be configured.

Cross-class data disparity¶

A model Leaderboard tab (Bias and Fairness > Cross-Class Data Disparity) that calculates, for each protected feature, evaluation metrics and ROC curve-related scores segmented by class. Bias and Fairness settings must be configured.

Cross-Validation (CV)¶

A type of validation partition that is run to test (validate) model performance. Using subsets ("folds") of the validation data, DataRobot creates one model per fold, with the data assigned to that fold used for validation and the rest of the data used for training. By default, DataRobot uses five-fold cross-validation and presents the mean of those five scores on the Leaderboard. See also validation.

Custom inference models¶

User-created, pre-trained models uploaded as a collection of files via the Custom Model Workshop. Upload a model artifact to create, test, and deploy custom inference models to the centralized deployment hub in DataRobot. An inference model can have a predefined input/output schema or it can be unstructured. To customize prior to model training, use custom tasks.

Custom model environment¶

A versioned, containerized environment (e.g., a Docker image) that includes all the necessary libraries, packages, and dependencies required to run a custom model or task within DataRobot. Administrators manage these environments to ensure reproducibility and governance.

Custom model workshop¶

In the Model Registry, a location where you can upload user-created, pre-trained models as a collection of files. You can use these model artifacts to create, test, and deploy custom inference models to centralized deployment hub in DataRobot.

Custom task¶

A data transformation or ML algorithm, for example, XGBoost or One-hot encoding, that can be used as a step in an ML blueprint inside DataRobot and used for model training. Tasks are written in Python or R and are added via the Custom Model Workshop. Once saved, the task can be used when modifying a blueprint with Composable ML. To deploy a pre-trained model where re-training is not required, use custom inference models.

CV¶

See Cross Validation.

D¶

Data classification¶

The process of categorizing data based on sensitivity, regulatory requirements, and business value to determine appropriate handling, storage, and access controls for LLM and AI systems. DataRobot provides automated PII detection and data governance features to help organizations classify and protect sensitive information in their datasets.

Data drift¶

The difference between values in new inference data used to generate predictions for models in production and the training data initially used to train the deployed model. Predictive models learn patterns in training data and use that information to predict target values for new data. When the training data and the production data change over time, causing the model to lose predictive power, the data surrounding the model is said to be drifting. Data drift can happen for a variety of reasons, including data quality issues, changes in feature composition, and even changes in the context of the target variable.

Data management¶

The umbrella term related to loading, cleaning, transforming, and storing data within DataRobot. It also refers to the practices that companies follow when collecting, storing, using, and deleting data.

Data preparation¶

The process of transforming raw data to the point where it can be run through machine learning algorithms to uncover insights or make predictions. Also called "data preprocessing," this term covers a broad range of activities like normalizing data, standardizing data, statistically or mathematically transforming data, processing and/or preprocessing data, and feature engineering.

Data Quality Handling Report¶

A model Leaderboard tab (Describe > Data Quality Handling Report) that analyzes the training data and provides the following information for each feature: feature name, variable type, row count, percentage, and data transformation information.

Data Registry¶

In Workbench, a central catalog for datasets, allowing you to link them to specific Use Cases.

Data residency¶

The physical or geographical location where LLM and AI data is stored and processed, often subject to regulatory requirements and compliance standards. DataRobot supports various deployment options including cloud, on-premises, and hybrid configurations to meet specific data residency requirements.

Data retention policies¶

Policies that define how long LLM and AI data should be kept, when it should be archived, and when it should be deleted to comply with regulations and manage storage costs.

Data wrangling¶

Data preparation operations of a scope that ties to creating a dataset at an appropriate unit of analysis for a given machine learning use case.

DataRobot Classic¶

The original DataRobot value-driven AI product. It provides a complete AI lifecycle platform, leveraging machine learning that has broad interoperability and end-to-end capabilities for ML experimentation and production. DataRobot Classic is being migrated to the new user interface, Workbench.

DataRobot User Model (DRUM)¶

A tool that allows you to test Python, R, and Java custom models and tasks locally. The test allows you to verify that a custom model can successfully run and make predictions in DataRobot before uploading it.

Dataset¶

Data, a file or the content of a data source, at a particular point in time. A data source can produce multiple datasets; an AI Catalog dataset has exactly one data source. In AI Catalog, a dataset is materialized data that is stored with a catalog version record. There may be multiple catalog version records associated with an entity, indicating that DataRobot has reloaded or refreshed the data. The older versions are stored to support existing projects, new projects use the most recent version. A dataset can be in one of two states:

• A "snapshotted" (or materialized) dataset is an immutable snapshot of data that has previously been retrieved and saved.
• A "remote" (or unmaterialized) dataset has been configured with a location from which data is retrieved on-demand (AI Catalog).

Data connection¶

A configured connection to a database—it has a name, a specified driver, and a JDBC URL. You can register data connections with DataRobot for ease of re-use. A data connection has one connector but can have many data sources.

Data source¶

A configured connection to the backing data (the location of data within a given endpoint). A data source specifies, via SQL query or selected table and schema data, which data to extract from the data connection to use for modeling or predictions. Examples include the path to a file on HDFS, an object stored in S3, and the table and schema within a database. A data source has one data connection and one connector but can have many datasets. It is likely that the features and columns in a datasource do not change over time, but that the rows within change as data is added or deleted.

Data stage¶

Intermediary storage that supports multipart upload of large datasets, reducing the chance of failure when working with large amounts of data. Upon upload, the dataset is uploaded in parts to the data stage, and once the dataset is whole and finalized, it is pushed to the AI Catalog or Batch Predictions. At any time after the first part is uploaded to the data stage, the system can instruct Batch Predictions to use the data from the data stage to fill in predictions.

Data store¶

A general term used to describe a remote location where your data is stored. A data store may contain one or more databases, or one or more files of varying formats.

Data/time partitioning¶

The only valid partitioning method for time-aware projects. With date/time, rows are assigned to backtests chronologically instead of, for example, randomly. Backtests are configurable, including number, start and end times, and sampling method.

Dashboard¶

Visual monitoring interfaces that provide real-time insights into LLM and AI system performance, health, and operational metrics for administrators and stakeholders. DataRobot provides comprehensive dashboards for monitoring model performance, data drift, prediction accuracy, and system health across all deployments.

Deep learning¶

A set of algorithms that run data through several "layers" of neural network algorithms, each of which passes a simplified representation of the data to the next layer. Deep learning algorithms are essential to Visual AI capabilities, and their processing can be viewed from the Training Dashboard visualization.

Deploying (from a playground)¶

LLM blueprints and all their associated settings are registered in Registry and can be deployed with DataRobot's production suite of products.

Deployment inventory¶

The central hub for managing deployments. Located on the Deployments page, the inventory serves as a coordination point for stakeholders involved in operationalizing models. From the inventory, you can monitor deployed model performance and take action as necessary, managing all actively deployed models from a single point.

Detection/segmentation¶

A computer vision technique that involves the selection of a subset of the input image data for further processing (for example, one or more images within a set of images or regions within an image).

Disaster recovery¶

Plans and procedures for recovering LLM and AI services after system failures, natural disasters, or other catastrophic events to ensure business continuity. DataRobot provides backup and restore capabilities, along with high availability configurations to minimize downtime and ensure continuous model serving.

Distributed tracing¶

A technique for monitoring and troubleshooting LLM and AI applications by tracking requests as they flow through multiple services and components.

Downloads tab¶

A model Leaderboard tab (Predict > Downloads) where you can download model artifacts.

Downsampling¶

See Smart downsampling.

Driver¶

The software that allows the DataRobot application to interact with a database; each data connection is associated with one driver (created and installed by your administrator). The driver configuration saves the JAR file storage location in DataRobot and any additional dependency files associated with the driver. DataRobot supports JDBC drivers.

Dynamic dataset¶

A dynamic dataset is a "live" connection to the source data, however, DataRobot samples the data for profile statistics (EDA1). The catalog stores a pointer to the data and pulls it upon request, for example, when you create a project.

E¶

EDA (Exploratory Data Analysis)¶

The DataRobot approach to analyzing and summarizing the main characteristics of a dataset. Generally speaking, there are two stages of EDA:

• EDA1 provides summary statistics based on a sample of data. In EDA1, DataRobot counts, categorizes, and applies automatic feature transformations (where appropriate) to data.
• EDA2 is a recalculation of the statistics collected in EDA1 but using the entire dataset, excluding holdout. The results of this analysis are the criteria used for model building.

Embedding¶

A numerical (vector) representation of text, or a collection of numerical representations of text. The action of generating embeddings means taking a chunk of unstructured text and using a text embedding model to convert the text to a numerical representation. The chunk is the input to the embedding model and the embedding is the "prediction" or output of the model.

Endpoint¶

A specific URL where a service can be accessed. In machine learning, an endpoint is typically used to send data to a deployed model and receive predictions. It is the primary interface for interacting with a model programmatically via an API.

Ensemble models¶

See blender.

Environment¶

A Docker container where a custom task runs.

Environment management¶

The practice of managing different environments (development, staging, production) for LLM and AI systems to ensure proper testing, deployment, and operational procedures.

ESDA¶

Exploratory Spatial Data Analysis (ESDA) is the exploratory data phase for Location AI. DataRobot provides a variety of tools for conducting ESDA within the DataRobot AutoML environment, including geometry map visualizations, categorical/numeric thematic maps, and smart aggregation of large geospatial datasets.

Eureqa¶

Model blueprints for Eureqa generalized additive models (Eureqa GAM), Eureqa regression, and Eureqa classification models. These blueprints use a proprietary Eureqa machine learning algorithm to construct models that balance predictive accuracy against complexity.

Event streaming¶

Real-time data processing systems that handle continuous streams of events from LLM and AI applications for monitoring, analytics, and operational insights.

EWMA (Exponentially Weighted Moving Average)¶

A moving average that places a greater weight and significance on the most recent data points, measuring trend direction over time. The "exponential" aspect indicates that the weighting factor of previous inputs decreases exponentially. This is important because otherwise a very recent value would have no more influence on the variance than an older value.

Experiment¶

An asset of a Use Case that is the result of having run the DataRobot modeling process. A Use Case can have zero or more experiments.

Experiment tracking¶

The process of recording and managing metadata, parameters, and results from machine learning experiments to enable reproducibility and comparison.

Exploratory data insights¶

See EDA.

External stage¶

An external stage is a cloud location outside of the Snowflake environment used for loading and unloading data for Snowflake. The cloud location can be either Amazon S3 or Microsoft Azure storage.

F¶

Fairness score¶

A numerical computation of model fairness against the protected class, based on the underlying fairness metric.

Fairness threshold¶

The measure of whether a model performs within appropriate fairness bounds for each protected class. It does not affect the fairness score or performance of any protected class.

Fairness value¶

Fairness scores normalized against the most favorable protected class (i.e., the class with the highest fairness score).

Favorable outcome¶

A value of the target that is treated as the favorable outcome for the model, used in bias and fairness modeling. Predictions from a binary classification model can be categorized as being a favorable outcome (i.e., good/preferable) or an unfavorable outcome (i.e., bad/undesirable) for the protected class.

FDW¶

See Feature Derivation Window.

Feature¶

A column in a dataset, also called "variable" or "feature variable." The target feature is the name of the column in the dataset that you would like to predict.

Feature Derivation Window¶

Also known as FDW; used in time series modeling. A rolling window of past values that models use to derive features for the modeling dataset. Consider the window relative to the Forecast Point, it defines the number of recent values the model can use for forecasting.

Feature Discovery¶

A DataRobot capability that discovers and generates new features from multiple datasets, eliminating the need to perform manual feature engineering to consolidate multiple datasets into one. A relationship editor visualizes these relationships and the end product is additional, derived features that result from the created linkages.

Feature Effects¶

A model Leaderboard tab (Understand > Feature Effects) that shows the effect of changes in the value of each feature on the model's predictions. It displays a graph depicting how a model "understands" the relationship between each feature and the target, with the features sorted by Feature Impact.

Feature engineering¶

The generation of additional features in a dataset, which as a result, improve model accuracy and performance. Time series and Feature Discovery both rely on feature engineering as the basis of their functionality.

Feature extraction¶

Models that perform image preprocessing (or image feature extraction and image preprocessing) are also known as "image feature extraction models" or "image-specific models."

Feature Extraction and Reduction (FEAR)¶

The feature generation process for time series modeling (e.g., lags, moving averages). It extracts new features (now) and then reduces the set of extracted features (later). See Time series feature derivation.

Feature flag¶

A DataRobot mechanism that allows administrators to enable or disable specific features for certain users, organizations, or the entire platform. Feature flags are used to manage phased rollouts, beta testing, and custom configurations. Toggling a feature flag is performed by DataRobot Support for SaaS customers.

Feature Impact¶

A measurement that identifies which features in a dataset have the greatest effect on model decisions. In DataRobot, the measurement is reported as a visualization available from the Leaderboard.

Feature imputation¶

A mechanism in time series modeling that uses forward filling to enable imputation for all features (target and others) when using the time series data prep tool. This results in a dataset with no missing values (with the possible exception of leading values at the start of each series where there is no value to forward fill).

Feature list¶

A subset of features from a dataset used to build models. DataRobot creates several lists during EDA2 including all informative features, informative features excluding those with a leakage risk, a raw list of all original features, and a reduced list. Uses can create project-specific lists as well.

Few-shot learning¶

A capability of a model to learn to perform a task from a small number of examples provided in the prompt.

Fine-tuning¶

The process of further training a pre-trained model on a specific dataset or task to improve its performance for specialized applications.

Fitting¶

See model fitting.

Forecast Distance¶

A unique time step—a relative position—within the Forecast Window in a time series modeling project. A model outputs one row for each Forecast Distance.

Forecast Point¶

In time series modeling, the point you are making a prediction from; a relative time "if it was now..."; DataRobot trains models using all potential forecast points in the training data. In production, it is typically the most recent time.

Forecast vs Actual¶

A model Leaderboard tab (Evaluate > Forecast vs Actual) commonly used in time series projects that allows you to compare how different predictions behave from different forecast points to different times in the future. Although similar to the Accuracy Over Time chart, which displays a single forecast at a time, the Forecast vs Actual chart shows multiple forecast distances in one view.

Forecast Window¶

Also known as FW; used in time series modeling. Beginning from the Forecast Point, defines the range (the Forecast Distance) of future predictions—"this is the range of time I care about." DataRobot then optimizes models for that range and ranks them on the Leaderboard on the average across that range.

Forecasting¶

Predictions based on time, into the future; use inputs from recent rows to predict future values. Forecasting is a subset of predictions, using trends in observation to characterize expected outcomes or expected responses.

Foundation model¶

A type of artificial intelligence model that is pre-trained on a broad range of Internet text. Examples of text foundation models are GPT-4 and PaLM. These models are referred to as "foundation" because they can be fine-tuned for a wide variety of specific tasks, much like how a foundation supports a range of structures above it.

Frozen run¶

A process that "freezes" parameter settings from a model's early, small sample size-based run. Because parameter settings based on smaller samples tend to also perform well on larger samples of the same data.

FW¶

See Forecast Window.

G¶

Generative AI¶

A type of artificial intelligence that leverages trained models to generate new content or data that is based on the training data. Generative AI can generate text, images, audio, and even code. The key difference is that outputs are created based on prompting rather than analysis and processing of input features. Generative AI incorporates any novel technologies including transformers, LLMs (large language models,) and GANs (generative adversarial networks).

Governance lens¶

A filtered view of DataRobot's deployment inventory on the Deployments page, summarizing the social and operational aspects of a deployment. These include the deployment owner, how the model was built, the model's age, and the humility monitoring status.

GPU (graphics processing unit)¶

A mechanism for processing computational tasks. GPUs are GPUs are highly optimized to do mathematical calculations and are excellent at parallelism, but only for less complex tasks. Datasets that include text and/or images and require deep learning models specifically benefit since it's mainly batches of matrix multiplication, and these can be parallelized very easily.

Grid search¶

An exhaustive search method used for hyperparameters.

Grounding¶

The process of ensuring that a language model's responses are based on specific, verifiable data or context, often by retrieving relevant information from a database or document.

Group¶

A collection of users who share common permissions and access to projects, deployments, and other resources within an organization. Groups simplify user management by allowing administrators to manage permissions for multiple users at once.

H¶

Hallucination¶

When a language model generates information that is plausible-sounding but factually incorrect or not grounded in the provided data.

Health checks¶

Automated monitoring systems that verify the health and availability of LLM and AI services by periodically checking their status and responsiveness.

High availability¶

System design principles and practices that ensure LLM and AI services remain available and operational even during hardware failures, software issues, or other disruptions.

Holdout¶

A subset of data that is unavailable to models during the training and validation process. Use the Holdout score for a final estimate of model performance only after you have selected your best model. See also Validation.

HTTP Status Codes¶

Codes issued by a server in response to a client's request made to the server (e.g., 200 OK, 404 Not Found, 500 Internal Server Error). They are essential for debugging API interactions.

Humility¶

A user-defined set of rules for deployments that allow models to be capable of recognizing, in real-time, when they make uncertain predictions or receive data they have not seen before. Unlike data drift, model humility does not deal with broad statistical properties over time—it is instead triggered for individual predictions, allowing you to set desired behaviors with rules that depend on different triggers.

I¶

Image data¶

A sequence of digital images (e.g., video), a set of digital images, a single digital image, and/or one or more portions of any of these—data used as part of Visual AI. A digital image may include an organized set of picture elements ("pixels") stored in a file. Any suitable format and type of digital image file may be used, including but not limited to raster formats (e.g., TIFF, JPEG, GIF, PNG, BMP, etc.), vector formats (e.g., CGM, SVG, etc.), compound formats (e.g., EPS, PDF, PostScript, etc.), and/or stereo formats (e.g., MPO, PNS, JPS).

Image preprocessing¶

A computer vision technique, part of Visual AI. Some examples include image re-sampling, noise reduction, contrast enhancement, and scaling (e.g., generating a scale space representation). Extracted features may be:

• Low-level: raw pixels, pixel intensities, pixel colors, gradients, textures, color histograms, motion vectors, edges, lines, corners, ridges, etc.
• Mid-level: shapes, surfaces, volumes, etc.
• High-level: objects, scenes, events, etc.

Incremental learning¶

A model training method specifically tailored for large datasets—those between 10GB and 100GB—that chunks data and creates training iterations. After model building begins, compare trained iterations and optionally assign a different active version or continue training. The active iteration is the basis for other insights and is used for making predictions.

Infrastructure as Code (IaC)¶

The practice of managing and provisioning LLM and AI infrastructure through machine-readable definition files rather than physical hardware configuration or interactive configuration tools.

Inference data¶

Data that is scored by applying an algorithmic model built from a historical dataset in order to uncover practical insights. See also Scoring data.

In-sample predictions¶

Predictions made on data that the model has already seen during its training process. This typically occurs when a model is trained on a very high percentage of the available data (e.g., above 80%), leaving little or no "unseen" data for validation. In such cases, the validation score is calculated from the same data used for training, which can result in an overly optimistic assessment of model performance. In DataRobot, these scores are marked with an asterisk on the Leaderboard to indicate that they may not reflect true generalization performance. Compare to stacked (out-of-sample) predictions.

Integration patterns¶

Common architectural patterns and best practices for integrating LLM and AI services with existing systems, applications, and data sources.

Irregular data¶

Data in which no consistent spacing and no time step is detected. Used in time-aware modeling.

J¶

JSON¶

JavaScript Object Notation, a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language Standard ECMA-262 3rd Edition - December 1999.

K¶

KA¶

See Known in advance features.

Kernel¶

Provides programming language support to execute the code in a notebook.

Known in advance features¶

Also known as KA; used in time series modeling. A variable for which you know the value in advance and does not need to be lagged, such as holiday dates. Or, for example, you might know that a product will be on sale next week and so you can provide the pricing information in advance.

L¶

Large language model (LLM)¶

An algorithm that uses deep learning techniques and large datasets to understand, summarize, generate, and predict new content.

Latency¶

The time delay between sending a request to a model or API and receiving a response, often measured in milliseconds.

Leaderboard¶

The list of trained blueprints (models) for a project, ranked according to a project metric.

Leakage¶

See target leakage.

Learning curves¶

A graph to help determine whether it is worthwhile to increase the size of a dataset. The Learning Curve graph illustrates, for the top-performing models, how model performance varies as the sample size changes.

License¶

A commercial agreement that grants access to the DataRobot platform. The license defines the scope of usage, including the number of authorized users, available features, and limits on computational resources.

Lift chart¶

Depicts how well a model segments the target population and how capable it is of predicting the target to help visualize model effectiveness.

Linkage keys¶

(Feature Discovery) The features in the primary dataset used as keys to join and create relationships.

LLM blueprint¶

The saved blueprint, available to be used for deployment. LLM blueprints represent the full context for what is needed to generate a response from an LLM; the resulting output can be compared within the playground. This information is captured in the LLM blueprint settings.

LLM blueprint components¶

The entities that make up the LLM blueprint settings, this refers to the vector database, embedding model user to generate the vector database, LLM settings, system prompt, etc. These components can either be offered natively within DataRobot or can be brought in from external sources.

LLM blueprint settings¶

The parameters sent to the LLM to generate a response (in conjunction with the user-entered prompt). They include a single LLM, LLM settings, optionally a system prompt, and optionally a vector database. If no vector database is assigned, then the LLM uses its learnings from training to generate a response. LLM blueprint settings are configurable so that you can experiment with different configurations.

LLM payload¶

The bundle of contents sent to the LLM endpoint to generate a response. This includes the user prompt, LLM settings, system prompt, and information retrieved from the vector database.

LLM responses¶

Generated text from the LLM based on the payload sent to an LLM endpoint.

LLM settings¶

Parameters that define how an LLM intakes a user prompt and generates a response. They can be adjusted within the LLM blueprint to alter the response. These parameters are currently represented by the "Temperature", "Token selection probability cutoff (Top P)", and "Max completion tokens" settings.

Load balancing¶

The distribution of incoming requests across multiple LLM and AI service instances to optimize resource utilization, maximize throughput, minimize response time, and avoid overload.

Location AI¶

DataRobot's support for geospatial analysis by natively ingesting common geospatial formats and recognizing coordinates, allowing ESDA, and providing spatially-explicit modeling tasks and visualizations.

Log¶

A model Leaderboard tab (Describe > Log) that displays the status of successful operations with green INFO tags, along with information about errors marked with red ERROR tags.

Log aggregation¶

The centralized collection and storage of logs from multiple LLM and AI services to enable comprehensive monitoring, analysis, and troubleshooting.

Loss function¶

A method of evaluating how well a specific algorithm models the given data. It computes a number representing the "cost" of the model's predictions being wrong; the goal of training is to minimize this value.

M¶

Machine Learning Operations¶

See MLOps.

Majority class¶

If you have a categorical variable (e.g., true/false or cat/mouse ), the value that's more frequent is the majority class. For example, if a dataset has 80 rows of value cat and 20 rows of value mouse, then cat is the majority class. See also minority class.

Make Predictions tab¶

A model Leaderboard tab (Predict > Make Predictions) that allows you to make predictions before deploying a model to a production environment.

Management agent¶

A downloadable client included in the MLOps agent tarball (accessed via API keys and tools) that allows you to manage external models (i.e., those running outside of DataRobot MLOps). This tool provides a standard mechanism to automate model deployment to any type of infrastructure. The management agent sends periodic updates about deployment health and status via the API and reports them as MLOps events on the Service Health page.

Manual¶

A modeling mode that causes DataRobot to complete EDA2 and prepare data for modeling, but does not execute model building. Instead, users select specific models to build from the model Repository.

Materialized¶

Data that DataRobot has pulled from the data asset and is currently keeping a copy of in the catalog. See also snapshot and unmaterialized data.

Metadata¶

Details of the data asset, such as creation and modification dates, number and types of features, snapshot status, and more.

Metric¶

See optimization metric.

Metrics collection¶

The systematic gathering of performance, business, and operational metrics from LLM and AI systems to enable monitoring, analysis, and decision-making.

Minority class¶

If you have a categorical variable (e.g., true/false or cat/mouse ), the value that's less frequent is the minority class. For example, if a dataset has 80 rows of value cat and 20 rows of value mouse, then mouse is the minority class. See also majority class.

MLOps (Machine Learning Operations)¶

A scalable and governed means to rapidly deploy and manage ML applications in production environments.

MLOps agent¶

The downloadable package (tarball) that contains two clients: the Monitoring Agent and the Management Agent. The MLOps Agent enables you to monitor and manage external models (i.e., those running outside of DataRobot MLOps) by providing these tools for deployment, monitoring, and reporting. See also Monitoring Agent and Management Agent.

Model¶

A trained machine learning model that can make predictions on new data. In DataRobot, models are built using various algorithms and can predict outcomes like customer churn, sales forecasts, or fraud detection.

Model approval workflows¶

Structured processes for reviewing, validating, and approving LLM and AI models before deployment to production, ensuring quality, compliance, and business alignment.

Model catalog¶

A centralized repository that provides a comprehensive view of all available LLM and AI models, including their versions, metadata, performance metrics, and deployment status.

Model comparison¶

A Leaderboard tab that allows you to compare two models using different evaluation tools, helping identify the model that offers the highest business returns or candidates for blender models.

Model deprecation¶

The process of phasing out and retiring old LLM and AI models from production use, including communication to stakeholders and migration strategies.

Model fitting¶

A measure of how well a model generalizes similar data to the data on which it was trained. A model that is well-fitted produces more accurate outcomes. A model that is overfitted matches the data too closely. A model that is underfitted doesn't match closely enough.

Model Info¶

A model Leaderboard tab (Describe > Model Info) that displays an overview for a given model, including model file size, prediction time, and sample size.

Model lineage¶

The complete history and provenance of LLM and AI models, including their training data, algorithms, parameters, and evolution over time for audit and compliance purposes. DataRobot tracks model lineage through the Model Registry, maintaining detailed records of training data, feature engineering steps, model versions, and deployment history for comprehensive audit trails.

Model overview¶

A page within an experiment that displays the model Leaderboard, and once a model is selected, displays visualizations for that model.

Model package¶

Archived model artifacts with associated metadata stored in the Model Registry. Model packages can be created manually or automatically, for example, through the deployment of a custom model. You can deploy, share, and permanently archive model packages.

Model Registry¶

An organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages; Registry lists each package available for use. Each package functions the same way, regardless of the origin of its model. The Model Registry also contains the Custom Model Workshop where you can create and deploy custom models. Model packages can be created manually or automatically depending on the type of model.

Model scoring¶

The process of applying an optimization metric to a partition of the data and assigning a numeric score that can be used to evaluate a model performance.

Model versioning¶

The systematic tracking and management of different versions of LLM and AI models to enable rollbacks, comparisons, and controlled deployments.

Modeling¶

The process of building predictive models using machine learning algorithms. This involves training algorithms on historical data to identify patterns and relationships that can be used to predict future outcomes. DataRobot automates much of this process through AutoML, allowing users to build, evaluate, and deploy predictive models efficiently.

Modeling dataset¶

A transform of the original dataset that pre-shifts data to future values, generates lagged time series features, and computes time-series analysis metadata. Commonly referred to as feature derivation, it is used by time series but not OTV. See the time series feature engineering reference for a list of operators used and feature names created by the feature derivation process. See also FEAR.

Modeling mode¶

A setting that controls the sample percentages of the training set that DataRobot uses to build models. DataRobot offers four modeling modes: Autopilot, Quick (the default), Manual, and Comprehensive.

Moderation¶

The process of monitoring and filtering model outputs to ensure they comply with safety, ethical, and policy guidelines.

Monitoring agent¶

A downloadable client included in the MLOps agent tarball (accessed via API keys and tools) that allows you to monitor external models (i.e., those running outside of DataRobot MLOps). With this functionality, predictions and information from these models can be reported as part of deployments. You can use this tool to monitor accuracy, data drift, prediction distribution, latency, and more, regardless of where the model is running.

Monotonic modeling¶

A method to force certain XGBoost models to learn only monotonic (always increasing or always decreasing) relationships between specific features and the target.

Multiclass¶

See classification.

Multilabel¶

A classification task where each row in a dataset is associated with one, several, or zero labels. Common multilabel classification problems are text categorization (a movie is both "crime" and "drama") and image categorization (an image shows a house and a car).

Multimodal¶

A model type that supports multiple var types at the same time, in the same model.

Multiseries¶

Datasets that contain multiple time series (for example, to forecast the sales of multiple stores) based on a common set of input features.

N¶

Naive model¶

See baseline model.

NextGen¶

The updated DataRobot user interface comprised of Workbench for experiment based iterative workflows, Registry for model evolution tracking and the centralized management of versioned models, and Console for monitoring and managing deployed models. NextGen also provides the gateway for creating GenAI experiments, Notebooks, and apps.

No-Code AI Apps¶

A no-code interface to create AI-powered applications that enable core DataRobot services without having to build models and evaluate their performance. Applications are easily shared and do not require consumers to own full DataRobot licenses in order to use them.

N-gram¶

A sequence of words, where N is the number of words. For example, "machine learning" is a 2-gram. Text features are divided into n-grams to prepare for Natural Language Processing (NLP).

Notebook¶

An interactive, computational environment that hosts code execution and rich media. DataRobot provides its own in-app environment to create, manage, and execute Jupyter-compatible hosted notebooks.

Nowcasting¶

A method of time series modeling that predicts the current value of a target based on past and present data. Technically, it is a forecast window in which the start and end times are 0 (now).

O¶

Offset¶

Feature(s) that should be treated as a fixed component for modeling (coefficient of 1 in generalized linear models or gradient boosting machine models). Offsets are often used to incorporate pricing constraints or to boost existing models.

One-shot learning¶

A capability of a model to learn to perform a task from only a single example.

Operation¶

A single data manipulation instruction that specifies to either transform, filter, or pivot one or more records into zero or more records (e.g., find and replace or compute new feature).

Optimization metric¶

An error metric used in DataRobot to determine how well a model predicts actual values. After you choose a target feature, DataRobot selects an optimization metric based on the modeling task.

Ordering feature¶

The primary date/time feature that DataRobot will use for modeling. Options are detected during EDA1.

Organization¶

A top-level entity in DataRobot that represents a single customer or tenant. It serves as a container for all users, groups, projects, deployments, and other assets, enabling centralized billing and resource management.

OTV¶

Also known as out-of-time validation. A method for modeling time-relevant data. With OTV you are not forecasting, as with time series. Instead, you are predicting the target value on each individual row.

Overfitting¶

A situation in which a model fits its training data too well and therefore loses its ability to perform accurately against unseen data. This happens when a model trains too long on the training data and learns (and models on) its "noise," making the model unable to generalize.

P¶

Partition¶

Segments of training data, broken down to maximize accuracy. The segments (splits) of the dataset. See also training, validation, cross-validation, and holdout.

Per-class bias¶

A model Leadboard tab (Bias and Fairness > Per-Class Bias) that helps to identify if a model is biased, and if so, how much and who it's biased towards or against. Bias and Fairness settings must be configured.

Permissions¶

A set of rights that control what actions a user or group can perform within DataRobot. Permissions are managed through roles and determine access to features like creating projects, deploying models, and managing system settings.

PID (project identifier)¶

An internal identifier used for uniquely identifying a project.

PII¶

Personal identifiable information, including name, pictures, home address, SSN or other identifying numbers, birth date, and more. DataRobot automates the detection of specific types of personal data to provide a layer of protection against the inadvertent inclusion of this information in a dataset.

Pipeline¶

A sequence of data processing and modeling steps, often automated, that transforms raw data into predictions or insights.

Playground¶

The place where you create and interact with LLM blueprints (LLMs and their associated settings), comparing the response of each to help determine which to use in production. Many LLM blueprints can live within a playground. A playground is an asset of a Use Case; multiple playgrounds can exist in a single Use Case.

Playground compare¶

The place to add LLM blueprints to the playground for comparison, submit prompts to these LLM blueprints, and evaluate the rendered responses. With RAG, a single prompt is sent to an LLM to generate a single response, without referencing previous prompts. This allows users to compare responses from multiple LLM blueprints.

Port¶

An interface that connects a DataRobot entity (a notebook, custom model, or custom app) to another network.

Portable Prediction Server (PPS)¶

A DataRobot execution environment for DataRobot model packages (.mlpkg files) distributed as a self-contained Docker image. It can be run disconnected from main installation environments.

Predicting¶

For non-time-series modeling. Use information in a row to determine the target for that row. Prediction uses explanatory variables to characterize expected outcomes or expected responses (e.g., a specific event in the future, gender, fraudulent transactions).

Prediction data¶

Data that contains prediction requests and results from the model.

Prediction environment¶

An environment configured to manage deployment predictions on an external system, outside of DataRobot. Prediction environments allow you to configure deployment permissions and approval processes. Once configured, you can specify a prediction environment for use by DataRobot models running on the Portable Prediction Server and for remote models monitored by the MLOps monitoring agent.

Prediction explanations¶

A visualization that helps to illustrate what drives predictions on a row-by-row basis—they provide a quantitative indicator of the effect variables have on a model, answering why a given model made a certain prediction. It helps to understand why a model made a particular prediction so that you can then validate whether the prediction makes sense. See also SHAP, XEMP.

Prediction intervals¶

Prediction intervals help DataRobot assess and describe the uncertainty in a single record prediction by including an upper and lower bound on a point estimate (e.g., a single prediction from a machine learning model). The prediction intervals provide a probable range of values that the target may fall into on future data points.

Prediction point¶

The point in time when you made or will make a prediction. Plan your prediction point based on the production model (for example, "one month before renewal" or "loan application submission time"). Once defined, create that entry in the training data to help avoid lookahead bias. With Feature Discovery, you define the prediction point to ensure the derived features only use data prior to that point.

Prediction server¶

The dedicated, scalable infrastructure responsible for hosting deployed models and serving real-time prediction requests via an API. It is optimized for low-latency and high-throughput scoring.

Prepared dataset¶

A dataset that has been materialized in its source after publishing a recipe.

Primary dataset¶

(Feature Discovery) The dataset used to start a project.

Primary features¶

(Feature Discovery) Features in the project's primary dataset.

Privacy controls¶

Mechanisms and policies for managing personal data in LLM and AI systems, including data anonymization, consent management, and compliance with privacy regulations.

Project¶

A referenceable item that includes a dataset, which is the source used for training, and any models built from the dataset. Projects can be created and accessed from the home page, the project control center, and the AI Catalog. They can be shared to users, groups, and an organization.

Prompt¶

The input entered during chatting used to generate the LLM response.

Prompt engineering¶

The practice of designing and refining input prompts to guide a language model toward producing desired outputs.

Prompt template¶

See system prompt.

Protected class¶

One categorical value of the protected feature, used in bias and fairness modeling.

Protected feature¶

The dataset column to measure fairness of model predictions against. Model fairness is calculated against the protected features from the dataset. Also known as "protected attribute."

Publish¶

Execution of the sequence of operations specified in a recipe resulting in the materialization of a dataset in a data source.

Q¶

Queue¶

The system that manages the execution of jobs, such as model training and batch predictions. The queue prioritizes and allocates tasks to available workers based on system load and user permissions, ensuring efficient use of computational resources.

Quick (Autopilot)¶

A shortened version of the full Autopilot modeling mode that runs models directly at 64%. With Quick, the 16% and 32% sample sizes are not executed. DataRobot selects models to run based on a variety of criteria, including target and performance metric, but as its name suggests, chooses only models with relatively short training runtimes to support quicker experimentation.

R¶

Rate limiting¶

A technique used to control the number of requests a client can make to an API within a specified time period, preventing abuse and ensuring fair usage.

Rating table¶

A model Leaderboard tab (Describe > Rating Table) where you can export the model's complete, validated parameters.

Real-time predictions¶

Method of making predictions when low latency is required. Use the Prediction API for real-time deployment predictions on a dedicated and/or a standalone prediction server.

Receiver Operating Characteristic Curve¶

See ROC Curve.

Recipe¶

A user-defined sequence of transformation operations that are applied to the data. A recipe is uniquely identified and versioned by the system. It includes metadata identifying the input data's source and schema, the output data's schema, the Use Case Container ID, and user ID.

Registry¶

Registry is a centralized location where you access versioned, deployment-ready model packages. From there, you can create custom models and jobs, generate compliance documentation, and deploy models to production.

Regression¶

A type of prediction problem that predicts continuous values (for example, 1.7, 6, 9.8…). See also classification.

Regularization¶

A technique used to prevent model overfitting by adding a penalty term to the loss function. Common types are L1 (Lasso) and L2 (Ridge) regularization.

Regular data¶

Data is regular if rows in the dataset fall on an evenly spaced time grid (e.g., there's one row for every hour across the entire dataset). See also time step and semi-regular data.

Relationships¶

(Feature Discovery) Relationships between datasets. Each relationship involves a pair of datasets, and a join key from each dataset. A key comprises one or more columns of a dataset. The keys from both datasets are ordered, and must have the same number of columns. The combination of keys is used to determine how two datasets are joined.

Remote models¶

Models running outside of DataRobot in external prediction environments, often monitored by the MLOps monitoring agent to report statistics back to DataRobot.

Repository¶

A library of modeling blueprints available for a selected project (based on the problem type). These models may be selected and built by DataRobot and also can be user-executed.

Resource optimization¶

The practice of optimizing LLM and AI resource usage for cost efficiency while maintaining performance and reliability requirements.

Resource provisioning¶

The allocation and management of computing resources (CPU, memory, storage, GPU) for LLM and AI workloads to ensure optimal performance and cost efficiency.

Response time optimization¶

Techniques and strategies for improving LLM response times, including caching, model optimization, and infrastructure improvements.

Retrieval Augmented Generation (RAG)¶

The process of sending a payload to an LLM that contains the prompt, system prompt, LLM settings, vector database (or subset of vector database), and the LLM returning corresponding text based on this payload. It includes the process of retrieving relevant information from a vector database and sending that along with the prompt, system prompt, and LLM settings to the LLM endpoint to generate a response grounded in the data in the vector database. This operation may optionally also incorporate orchestration to execute a chain of multiple prompts.

REST (Representational State Transfer)¶

An architectural style for designing networked applications, commonly used for web APIs, that uses standard HTTP methods (GET, POST, PUT, DELETE) to access and manipulate resources.

ROC Curve¶

Also known as Receiver Operating Characteristic Curve. A visualization that helps to explore classification, performance, and statistics related to a selected model at any point on the probability scale. In DataRobot, the visualization is available from the Leaderboard.

Role¶

Roles—Owner, Consumer, and Editor—describe the capabilities provided to each user for a given dataset. This supports the scenarios when the user creating a data source or data connection and the enduser are not the same, or there are multiple endusers of the asset.

Role-based access control (RBAC)¶

A security model that restricts access to LLM and AI systems based on the roles of individual users, providing granular permission management and security control. DataRobot implements RBAC through user groups, permissions, and organization-level access controls to ensure secure and appropriate access to features and assets across the platform.

S¶

Sample¶

The process of selecting a subset of data from a larger dataset for analysis, modeling, or preview purposes. DataRobot samples data in various contexts:

• EDA1 sampling: DataRobot samples up to 500MB of data for initial exploratory data analysis. If the dataset is under 500MB, it uses the entire dataset; otherwise, it uses a 500MB random sample.
• Live sample: During data wrangling, DataRobot retrieves a configurable number of rows (default 10,000) using different sampling methods (Random, First-N Rows, or Date/time for time series data) to provide interactive preview and analysis capabilities.
• Feature Impact sampling: For calculating feature importance, DataRobot samples training records (default 2,500 rows, maximum 100,000) using different sampling strategies based on data characteristics (random sampling for balanced data, smart downsampling for imbalanced data).
• Model evaluation sampling: Various model insights and evaluations use sampled data to balance computational efficiency with statistical accuracy.

Sample size¶

The percentage of the total training data used to build models. The percentage is based on the selected modeling mode or can be user-selected.

Scoring¶

See Model scoring, Scoring data.

Scoring code¶

A method for using DataRobot models outside of the application. It is available for select models from the Leaderboard as a downloadable JAR file containing Java code that can be used to score data from the command line.

An exportable JAR file, available for select models, that runs in Java. Scoring Code JARs contain prediction calculation logic identical to the DataRobot API—the code generation mechanism tests each model for accuracy as a part of the generation process.

Scoring data¶

A method for using DataRobot models outside of the application. For select models, Scoring Code is available as a downloadable, self-contained JAR file with Java code. This allows users to score new data from the command line using the exact same prediction logic as the DataRobot API.

SDK (Software Development Kit)¶

A collection of tools and libraries provided by a hardware or software vendor to enable developers to create applications for a specific platform. (e.g., the DataRobot Python SDK).

Seasonality¶

Repeating highs and lows observed at different times of year, within a week, day, etc. Periodicity. For example, temperature is very seasonal (hot in the summer, cold in the winter, hot during the day, cold at night). Applicable to time series modeling.

Secondary dataset¶

(Feature Discovery) A dataset that is added to a project and part of a relationship with the primary dataset.

Secondary features¶

(Feature Discovery) Features derived from a project's secondary datasets.

Segmented analysis¶

A deployment utility that filters data drift and accuracy statistics into unique segment attributes and values. Useful for identifying operational issues with training and prediction request data.

Segmented modeling¶

A method of modeling multiseries projects by generating a model for each segment. DataRobot selects the best model for each segment (the segment champion) and includes the segment champions in a single Combined Model that you can deploy.

Segment ID¶

A column in a dataset used to group series into segments for a multiseries project. A segment ID is required for the segmented modeling workflow, where DataRobot builds a separate model for each segment. See also Segmented modeling.

Semantic layer¶

A semantic layer is a business representation of the source data that maps complex data to common business terms, helping you more easily understand what the data means and the information it represents.

Semi-regular data¶

Data is semi-regular if most time steps are regular but there are some small gaps (e.g., business days, but no weekends). See also regular data and time steps.

Series ID¶

A column in a dataset used to divide a dataset into series for a multiseries project. The column contains labels indicating which series each row belongs to. See also Multiseries modeling.

Service health¶

A performance monitoring component for deployments that tracks metrics about a deployment's ability to respond to prediction requests quickly and reliably. Useful for identifying bottlenecks and assessing prediction capacity.

Service mesh¶

A dedicated infrastructure layer for managing communication between LLM and AI microservices, providing features like load balancing, service discovery, and security. Service meshes enable fine-grained control over service-to-service communication, including traffic management, observability, and policy enforcement for complex AI application architectures.

SHAP (Shapley Values)¶

A fast, open-source methodology for computing Prediction Explanations for tree-based, deep learning, and linear-based models. SHAP estimates how much each feature contributes to a given prediction differing from the average. It is additive, making it easy to see how much top-N features contribute to a prediction. See also Prediction Explanations, XEMP.

Sidecar model¶

A structural component that supports the LLM that is serving back responses. It helps to make determinations about whether a prompt was toxic, an injection attack, etc. With DataRobot, it uses hosted custom metrics to accomplish the monitoring.

Single Sign-On (SSO)¶

An authentication method that allows users to log in to DataRobot using their existing corporate identity provider (e.g., Okta, Azure AD). SSO simplifies user access by eliminating the need for separate DataRobot-specific credentials.

Slim run¶

A technique to improve time and memory use, slim runs apply to datasets exceeding 800MB. When triggered, models do no calculate an internal cross-validation and so do not have stacked predictions.

Smart downsampling¶

A technique to reduce total dataset size by reducing the size of the majority class, enabling you to build models faster without sacrificing accuracy. When enabled, all analysis and model building is based on the new dataset size after smart downsampling.

Snapshot¶

An asset created from a data source. For example, with a database it represents either the entire database or a selection of (potentially joined) tables, taken at a particular point in time. It is taken from a live database but creates a static, read-only copy of data. DataRobot creates a snapshot of each data asset type, while allowing you to disable the snapshot when importing the data.

Speed vs accuracy¶

A Leaderboard tab that generates an analysis plot to show the tradeoff between runtime and predictive accuracy and help you choose the best model with the lowest overhead.

Stability¶

A model Leaderboard tab (Evaluate > Stability) that provides an at-a-glance summary of how well a model performs on different backtests. The backtesting information in this chart is the same as that available from the Model Info tab.

Stacked predictions¶

A method for building multiple models on different subsets of the data. The prediction for any row is made using a model that excluded that data from training. In this way, each prediction is effectively an "out-of-sample" prediction. See an example in the predictions documentation. Compare to "in-sample" predictions. See also slim run.

Stationarity¶

The mean of the series does not change over time. A stationary series does not have a trend or seasonal variation. Applicable to time series modeling. See also trend.

Stop sequence¶

A specific token or set of tokens that signals a language model to stop generating further output.

Supervised learning¶

Machine learning using labeled data, meaning that for each record, the dataset contains a known value for the target feature. By knowing the target during training, the model can "learn" how other features relate to the target and make predictions on new data. See also unsupervised learning.

System prompt¶

The system prompt, an optional field, is a "universal" prompt prepended to all individual prompts. It instructs and formats the LLM response. The system prompt can impact the structure, tone, format, and content that is created during the generation of the response.

T¶

Target¶

The name of the column in the dataset that you would like to predict.

Target leakage¶

An outcome when using a feature whose value cannot be known at the time of prediction (for example, using the value for "churn reason" from the training dataset to predict whether a customer will churn). Including the feature in the model's feature list would incorrectly influence the prediction and can lead to overly optimistic models.

Task¶

An ML method, for example a data transformation such as one-hot encoding, or an estimation such as an XGBoost classifier, which is used to define a blueprint. There are hundreds of built-in tasks you can use, or you can define your own (custom) tasks.

Temperature¶

A parameter that controls the randomness of a language model's output. Lower values make the output more focused and deterministic, while higher values increase diversity and creativity in the generated text.

Terminal¶

A text-based interface used to interact with a server by entering commands.

Time-aware predictions¶

Assigns rows to backtests chronologically and makes row-by-row predictions. This method provides no feature engineering and can be used when forecasting is not needed.

Time-aware predictions with feature engineering¶

Assigns rows by forecast distance, builds separate models for each distance, and then makes row-by-row predictions. This method is best when combined with time-aware wrangling, which provides transparent and flexible feature engineering. Use when forecasting is not needed, but predictions based on forecast distance and full transparency of the transformation process is desired.

Time-aware wrangling¶

Perform time series feature engineering during the data preparation phase by creating recipes of operations and applying them first to a sample and then, when verified, to a full dataset—time-aware data. This method provides control over which time-based features are generated before modeling to allow adjustment before publishing, preventing the need to rerun modeling if what would otherwise be done automatically doesn't fit the use case.

Time series¶

A series of data points indexed in time order—ordinarily a sequence of measurements taken at successive, equally spaced intervals. Time series modeling is a recommended practice for data science problems where conditions may change over time.

Time series analysis¶

Methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.

Time series forecasting¶

The use of a model to predict future values based on previously observed values. In practice, a forecasting model may combine time series features with other data.

Time step¶

The detected median time delta between rows in the time series; DataRobot determines the time unit. The time step consists of a number and a time-delta unit, for example (15, "minutes"). If a step isn't detected, the dataset is considered irregular and time series mode may be disabled. See also regular data and semi-regular data.

Token¶

A token is the smallest unit of text an LLM can work with. When processing a user prompt, the LLM splits the input text into tokens and generates the output token by token. Different LLMs can separate the same text into tokens differently as they learn their vocabulary from the data they were trained on. Depending on the language and the LLM, a token can be a character, a group of characters, a word, or any other unit of text. An approximation of token length for GPT models is 1 token ~= 4 chars in English.

Throughput¶

The number of requests or predictions a system can process in a given period, often measured as requests per second (RPS) or tokens per second for LLMs.

Token usage tracking¶

The monitoring and recording of LLM token consumption to track costs, usage patterns, and optimize resource allocation. DataRobot provides token usage analytics and cost management features to help organizations monitor and control their LLM API expenses across different models and deployments.

Tokenization¶

The process of breaking text into smaller units called tokens, which can be words, subwords, or characters, for processing by a language model.

Top-k¶

A decoding parameter that limits the model's next-token choices to the k most likely options, sampling from only those candidates to generate more focused or creative responses.

Top-p (nucleus sampling)¶

A decoding parameter that limits the model's next-token choices to the smallest set whose cumulative probability exceeds a threshold p, allowing for dynamic selection of likely tokens.

Toxicity¶

The presence of harmful, offensive, or inappropriate language in model outputs, which safety and moderation systems aim to detect and prevent.

Tracking agent¶

See MLOps agent.

Training¶

The process of building models on data in which the target is known.

Training dashboard¶

A model Leaderboard tab (Evaluate > Training dashboard) that provides, for each executed iteration, information about a model's training and test loss, accuracy, learning rate, and momentum to help you get a better understanding about what may have happened during model training.

Training data¶

The portion (partition) of data used to build models. See also validation, cross-validation, and holdout.

Transfer learning¶

A project training on one dataset, extracting information that may be useful, and applying that learning to another.

Trend¶

An increase or decrease over time. Trends can be linear or non-linear and can show fluctuation. A series with a trend is not stationary.

Tuning¶

A trial-and-error process by which you change some hyperparameters, run the algorithm on the data again, then compare performance to determine which set of hyperparameters results in the most accurate model. In DataRobot, this functionality is available from the Advanced Tuning tab.

U¶

Unit of analysis¶

(Machine learning) The unit of observation at which you are making a prediction.

Unlimited multiclass¶

See classification.

Unmaterialized¶

Data that DataRobot samples for profile statistics, but does not keep. Instead, the catalog stores a pointer to the data and only pulls it upon user request at project start or when running batch predictions. See also materialized data.

Unstructured text¶

Text that cannot fit cleanly into a table. The most typical example is large blocks of text typically in some kind of document or form.

Unsupervised learning¶

The ability to infer patterns from a dataset without reference to known (labeled) outcomes and without a specified target. Types of unsupervised learning include anomaly detection, outlier detection, novelty detection, and clustering. With anomaly detection, DataRobot applies unsupervised learning to detect abnormalities in a dataset. With clustering, DataRobot uses unsupervised learning to discern natural groupings in the data. See also supervised learning.

Use Case¶

A container that groups objects that are part of the Workbench experimentation flow.

User¶

A DataRobot account that can be assigned to a specific user. Users can be assigned to one or more organizations and have specific permissions within those organizations.

User blueprint¶

A blueprint (and extra metadata) that has been created by a user and saved to the AI Catalog, where it can be both shared and further modified. This is not the same as a blueprint available from the Repository or via models on the Leaderboard, though both can be used as the basis for creation of a user blueprint. See also blueprint.

V¶

Validation¶

The validation (or testing) partition is a subsection of data that is withheld from training and used to evaluate a model's performance. Since this data was not used to build the model, it can provide an unbiased estimate of a model's accuracy. You often compare the results of validation when selecting a model. See also cross-validation.

Variable¶

See feature.

Variance (Statistical)¶

The variability of model prediction for a given data point. High-variance models are often too complex and are sensitive to the specific data they were trained on, leading to overfitting.

Vector database¶

A collection of chunks of unstructured text and corresponding text embeddings for each chunk, indexed for easy retrieval. Vector databases can optionally be used to ground the LLM responses to specific information and can be assigned to an LLM blueprint to leverage during a RAG operation. The creation of a vector database occurs when a collection of unstructured text is broken up into chunks, embeddings are generated for each chunk, and both the chunks and embeddings are stored in a database and are available for retrieval by some service. Updating the vector database is the action of adding (or removing) content to (or from) the originally created vector database. This means adding new chunks of text (or removing) and creating new embeddings (or removing) in the vector database.

Visual AI¶

DataRobot's ability to combine supported image types, either alone or in combination with other supported feature types, to create models that use images as input. The feature also includes specialized insights (e.g., image embeddings, attention maps, neural network visualizer) to help visually assess model performance.

W¶

Word cloud¶

A model Leaderboard tab (Understand > Word Cloud) that displays the most relevant words and short phrases in word cloud format.

Workbench¶

Workbench is an experiment-based user interface optimized to support iterative workflow. It enables users to group and share everything they need to solve a specific problem from a single location. Workbench is organized by Use Case, and each Use Case contains zero or more datasets, vector databases, playgrounds, models, notebooks, and applications. Workbench is the new generation of DataRobot Classic.

Worker¶

The processing power behind the DataRobot platform, used for creating projects, training models, and making predictions. They represent the portion of processing power allocated to a task. DataRobot uses different types of workers for different phases of the project workflow, including DSS workers (Dataset Service workers), EDA workers, secure modeling workers, and quick workers.

Wrangle¶

A capability that enables you to import, explore, and transform data in an easy-to-use GUI environment.

Webhook¶

A user-defined HTTP callback that allows one system to send real-time data to another system when a specific event occurs.

X¶

XEMP (eXemplar-based Explanations of Model Predictions)¶

A methodology for computing Prediction Explanations that works for all models. See also Prediction Explanations, SHAP.

Y¶

YAML¶

A human-readable data-serialization language, often used for configuration files and in applications where data is being stored or transmitted.

Z¶

Z score¶

A metric measuring whether a given class of the protected feature is "statistically significant" across the population. used in bias and fairness modeling.

Zero-shot learning¶

A capability of a model to perform a task without having seen any examples of that task during training, relying on generalization from related knowledge.