Release Notes

Kumo table and view creation now streamlined in a unified "Add Table/View" page.
Newly refined UI across the Kumo SaaS app.
Various minor fixes and UI improvements.

Multi-label ranking is now available in PQLv2.
Encoder use can now be specified for autoregressive labels in regression and forecasting tasks (by specifying past_encoder in the model plan).
Various backend performance enhancements and improvements.
Various minor fixes and UI improvements.

Enhanced monitoring for batch predictions to detect unusual gaps in fact tables.
For classification, link prediction, and regression tasks, heuristic baselines now available for comparing Kumo results to other baselines.
Various backend performance enhancements and improvements.
Various minor fixes and UI improvements.

Data distribution drift statistics now available for batch predictions.
Row-level explainability (XAI) metrics now available via the explorer tab.
Enhanced datatype changes are now available during preprocessing when creating tables.
When setting up dimension tables, end date can now be set up to restrict training and batch predictions to a specific timeframe.
Various minor fixes and UI improvements.

For ranking tasks (i.e., pqueries using LIST_DISTINCT with RANK TOP K), target item limit increased from 1M to 10M.
For certain types of pQueries (e.g., link prediction tasks), an Explorer section is available for evaluating predictions against historical and ground truth data.
Various minor fixes and UI improvements.

Improvements to AWS S3 connector allow for CSV/Parquet support and broader scaling (more tables) capability.
Various minor fixes and UI improvements.

The pquery syntax has been updated to make it easier to understand and more flexible in the way filters can be applied.
Various minor fixes and UI improvements.

New model planner available during pQuery training allows for fine-grained control over encoders, training strategy, and the AutoML search space.
Additional model planner (previously advanced options) configuration options available

XAI: various minor fixes and UI improvements.
XAI: metrics now available for multiclass and multilabel classification tasks
For node prediction tasks, test data splits can now be downloaded from the Review Evaluation Metrics page.
When selecting source tables, a new raw table option is available for connecting tables that don't conform to either fact or dimension table types.
Kumo views enable the running of traditional SQL queries that materialize a view in the Kumo data plane.

Batch predictions now include output statistics computed from a sample of table data.
Various minor fixes and UI improvements.

XAI - Cohort analysis for time columns now improved to be more interpretable.
XAI - Cohort analysis now working for tables that are two hops away from the prediction entity table.
A new refit feature enables automatic model refitting on entire data.
Descriptions can now be added and updated for any objects in the Kumo platform
During new pquery creation, automatically re-use already materialized graphs from prior pQuery creation jobs.
A new connector is available for connecting to Google Cloud BigQuery.
For multilabel classification pQueries (e.g. using the LIST_DISTINCT() operator on a maximum of 1,000 classes), evaluation metrics now include class-specific metrics.

XAI - In Column Analysis, actual versus predicted values are now displayed per column.
A new table column type called Embedding enables the use of embeddings as an input column.
For regression pQueries predicting a numeric output (using COUNT, SUM, etc. operators), evaluation results now include scatter plot charts that display actual versus predicted values.
During pQuery training, charts and tables are now provided to show how the training example target labels used to train the pQuery vary over time and across training/validation/holdout data splits.

A “Distribution of Predictions” chart showcasing a visualization of the predicted values alongside the actual target labels for all entities in a regression task (e.g., predictive queries with COUNT() or SUM() operator)
Expose boolean advanced option to handle prediction of unseen target entities at batch prediction time for link prediction tasks
Creating custom Kumo Views using SQL queries on top of tables already connected to the platform
Enable kicking off up to 10 asynchronous jobs (training/batch prediction) that will get queued and run sequentially one after another as older jobs complete
Enable concurrent execution of more than 1 job

A plot showcasing the distribution of values for timestamp columns for validating while ingesting new tables
S3 CSV data sources supported as connectors
Calibrating batch predictions for classification tasks using Platt Scaling
Parallelize batch prediction jobs involving large dataset size on multiple workers (up to 4)
XAI - Explaining how the underlying data contributes to the final predictions
- Contribution score of individual tables and the columns within them
- Cohort analysis for the range of values of each column and for the range of number of historic facts available in tables
Miscellaneous minor UX flow, bug, predictive accuracy fixes