Glossary
Use this glossary for terms that have a specific meaning in Kumo Relational and sdm-client. Follow the links for procedures, examples, and complete reference information.
A
Anchor time. The reference time associated with a prediction or context example, passed to materialize(). Kumo Relational uses the time boundary to determine which historical events are available to the prediction. See Configuration.
C
Candidates. The set of rows a ranking query is asked over, passed to materialize(candidates=...). Required for RANK TOP K — no ranking task assumes a candidate set. See Prediction Types.
Context. Rows where the answer is already known, gathered by materialize() from the schema’s historical data. See How Kumo Relational Works.
E
Entity table. A table representing objects for which Kumo Relational can make predictions. An entity table typically has a stable, unique primary key.
Event table. A table containing time-dependent activities or transactions related to one or more entity tables. Its time column identifies when each event became available to the prediction workflow.
Explanation. The structured attribution payload returned in result.explanation when outputs includes 'explanation', where the deployment produces one. Many shipping deployments refuse this output entirely. See Prediction Explainability.
F
Fan-out. The maximum number of neighbors sampled at one graph hop. Each value in num_neighbors sets the fan-out for the corresponding hop.
fit. Pins a materialized request’s context on the endpoint and returns a FittedModel for scoring several questions against it without re-uploading. Takes an optional holdout= naming every row the session will later score. See Make Predictions.
Foreign key. A column whose values refer to the primary key of another schema table. A Relationship connects a foreign key to its target entity table.
H
Holdout. The fit(holdout=...) argument naming every entity key a session will go on to score, so those rows can be excluded from the context pinned at fit() time. Without it, a later call can score a row that is sitting in the pinned context with its own answer attached.
Hop. One relationship traversal in the relational neighborhood around a prediction entity. The number of entries in num_neighbors determines the number of sampled hops.
I
id. The entity’s primary-key value on a result row, present when the model has an entity key. Join on this, or on row_index when there is none. See Prediction Results.
indices. Entity primary-key values passed to materialize(). They select the entities to score, up to 100,000 per call. See Make Predictions.
Inference configuration. Task-specific model settings passed through inference_config on materialize(). See Configuration.
K
kumo-relational. The model id sent on the wire and reported by a NIM’s /v1/models. Named through the KumoRelational() handle in sdm-client.
KumoRelational. The sdm-client handle for the relational model. Passed as the model= argument to predict(), fit(), and materialize().
N
Neighborhood. Relational context sampled around a prediction entity. Its depth and maximum fan-out are controlled by num_neighbors.
NIM. A deployed inference service that hosts Kumo Relational and exposes its endpoint. sdm-client connects to the NIM and sends prediction requests; it does not deploy or run the model itself. See Deploy, Install, and Connect.
P
Predictive Query Language (PQL). The language used to describe a predictive task over a schema. A runnable Kumo Relational query identifies a target and the entity or entities to score. See Write Predictive Queries.
Prediction entity. The entity for which Kumo Relational returns a prediction. In PQL, the entity is identified by the table key in the FOR or FOR EACH clause.
Prediction horizon. The future interval covered by a temporal prediction target, expressed by the start, end, and time unit in a PQL aggregation.
prediction_statistic. A materialize() option choosing what a regression’s prediction column reports of the model’s predicted distribution: "median" (default) or "mean". See Prediction Results.
PredictionResult. The object every predict() and fit().predict() call returns. to_pandas() gives the DataFrame; explanations carries gradients and column cohorts when requested and available; unscored_candidates lists any ranking candidates left out. See Prediction Results.
Primary key. A non-null, unique column (or ordered set of columns, for a composite key) that identifies each row in a schema table.
R
Relationship. A schema connection from a foreign key in an event or junction table to the primary key of an entity table. See Define Relationships.
Request. What to ask, independent of which model answers it. For Kumo Relational, built only through PredictiveQuery(...).materialize(...). See Make Predictions.
row_index. The zero-based position of a scored row within the whole (possibly batched) call, carried by every result row.
rows_per_call. A materialize() option lowering how many entities one NIM call scores, below the endpoint’s own cap. Only ever lowers it. See Batch Prediction.
S
Schema. The tables, table metadata, and relationships sdm-client uses to sample predictive context. Built with from_data() (in memory) or from_source() (sampled in place). One table with no relationships is the same type Kumo Tabular uses. See Build a Schema.
SDMClient. The public sdm-client class that owns the HTTP connection to one NIM endpoint. Serves both Kumo Relational and Kumo Tabular. See Python SDK API.
Semantic model. A Databricks metric view or Snowflake semantic view, converted into a Schema with Schema.from_source(source.semantic_model(name)).
Semantic type (stype). Metadata describing how sdm-client should interpret a column, such as ID, numerical, categorical, text, or timestamp. Assigned as a plain string. See Data Types and Semantic Types.
Source. Describes where a schema’s tables live and how to reach them, for Schema.from_source(): DatabricksSource, SnowflakeSource, PostgresSource, DuckDBSource, or SQLiteSource.
T
Target. The value or event to predict. In PQL, the target can be a column, a future aggregation, or a condition applied to an aggregation.
Task kind. The prediction category: classification, binary_classification, multiclass_classification, regression, forecasting, or temporal_link_prediction. See Prediction Types.
Time column. Table metadata identifying when an event became available to the prediction workflow. There is no supported end_time_column. See Time Columns.
U
unscored_candidates. On a ranking’s PredictionResult, the candidates no entity’s sampled context could answer, left out of every row. See Prediction Results.