​
v1.26 (2023-12-18)

Summary
  • 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.

​
v1.25 (2023-11-27)

Summary
  • BigQuery now available as a batch prediction output.

​
v1.24 (2023-11-13)

Summary
  • 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.

​
v1.23 (2023-10-30)

Summary
  • 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.

​
v1.22 (2023-10-16)

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

​
v1.21 (2023-10-02)

Summary
  • 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.

​
v1.20 (2023-09-18)

Summary
  • 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.

​
v1.19 (2023-09-04)

Summary
  • 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.

​
v1.18 (2023-08-21)

Summary
  • 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.