New recommendation task for limited item pool
When fine-tuning for recommendation problem, users can now choose from two specialised classes:
OneHotRecommendationTask, where the target is a fixed-size vector corresponding to the total number of recommendable entities.
RecommendationTask where the target is a probabilistic sketch representation.
The one-hot variant is especially beneficial when the number of recommendable entities is relatively low (e.g.,<5,000) where it often leads to better predictive performance.
Loss weighting for One-Hot Recommendation and Multilabel Classification
Users can now optionally return weights in the target function to control the relative importance of individual target elements. This is particularly useful in use cases like training acquisition models, where only products not previously purchased should be considered during model training.
Entity filtering
Users can now define which targets should be included or excluded from predictions and ground truth using the targets_to_include_fn and targets_to_exclude_fn parameters in both load_from_foundation_module and load_from_checkpoint. This change provides more precise control over evaluation behavior in multiclass classification, multi-label classification, one-hot recommendation, and sketch recommendation.
As part of this update, the targets_to_include parameter has been removed from TrainingParams to avoid redundancy.
Please note that while entity filtering functions affect scoring, ground truth, and metrics during the validation, prediction, and test phases, they do not impact model training. To exclude targets during training, loss weighting should be used instead.
Adjustable number of split points for random sampling strategy
The maximum number of split points per observation used during a single epoch has been increased from 1 to the square root of the number of event timestamps when target_sampling_strategy="random" is selected. As a result, the maximum_splitpoints_per_entity parameter will now also influence the number of split points in the random sampling strategy. This change ensures consistency across sampling strategies, as maximum_splitpoints_per_entity previously applied only to the valid strategy.
Flexible entity split
Entity split parameters such as training, validation, test, training_validation_end can now be changed during scenario model training. This enables iterative experimentation and fine-tuning without the need to retrain the foundation model, allowing user to adapt data splits to insights discovered during training.
Sketch merging support
Sketches derived from shared entity columns can now be added to other sketches. This capability is particularly useful when building advanced target functions, as it allows users to combine different behavioral signals into a single, richer representation. For example, in datasets with very few product purchase transactions, a hybrid sketch can be created by adding a transaction-based sketch to a product view-based sketch. The hybrid sketch captures a broader range of product interactions and serves as a strong proxy for the sparse transaction data, improving model robustness and generalization.
Retention policy support during training
User can now use the entity_history_limit parameter to define the maximum history time range for a single observation per data source during training. This allows simulation of retention policies used by certain databases that store only recent events.
Extended logging in ML experiment tracking tools
The number of validation batches is now logged in model lifecycle management tools, such as Neptune and MLflow.
Consistent entity split
Entity split into training, validation and test is now consistent between fit and training Foundation Model phases.
Fixes:
Fixed an error when date column containing time zone caused errors during fit phase.
Resolved an issue when Snowflake token authorization was used always if token was present, even if different authorization method was configured.
Fixed an issue when overwrite setting removed checkpoint files during training with multi-GPU.
Restored cache creation when enabled in configuration.
Hybrid train/test split
Users can now combine entity-based training and validation splits with a time-based test set. This mirrors production scenarios more closely by ensuring that the test set simulates future data while respecting entity isolation during training.
Limited end date of training and validation
Introduced the training_validation_end parameter to limit the latest date included in training and validation splits when using entity-based partitioning.
Flexible training validation interval
Introduced check_val_every_n_steps and check_val_every_n_epochs parameters for more granular control over validation frequency during training.
Reproducible results
A seed parameter has been added to key methods (fit_behavioral_representation, train_foundation_model, pretrain, fit, evaluate, predict, and test) to ensure consistent and reproducible outputs across runs.
Refactored run continuation logic
The overwrite and resume parameters must now be passed directly to the fit method rather than being read from TrainingParams.
Refined interpretability date specification
The target date for interpretation should now be provided via the prediction_date parameter instead of being inferred from split.
Flexible Kerberos configuration [BREAKING CHANGE]: In Hive connection configuration separate realm for Kerberos can now be defined with kinit_realm parameter while realm for connection string can be defined in ini file.
Normalized feature importance in interpretability
Feature importance scores are now normalized based on each feature’s input size, enabling fairer comparisons between features of different scales.
Improved Parquet cache behavior
Cache is automatically refreshed if the source Parquet file has changed—no manual deletion required.
The cache_path parameter is now optional. However, disabling the cache is not recommended, as it can significantly increase runtime and slow down data processing.
Optimized training behavior
The warm_start_steps parameter has been removed following internal improvements to the training loop.
Performance
Multiple internal optimizations have led to 2–3× faster performance on benchmark datasets, including:
Accelerated data loading pipeline
More efficient handling of time-based features for entities with long histories
Streamlined data validation and comparison logic
Fixes:
Resolved an issue where complex column types (e.g., lists of strings) caused errors during preprocessing. These types are now skipped with a warning.
Fixed excessive memory usage during the prediction phase.
Entity-based splits
Users will now be able to separate training, validation and testing sets based on time range or entity ID (e.g. create user hold-out rather than temporal one).
New method for selecting entity IDs
Users now have more flexible control over which entity IDs are included in training and testing:
A new entity_ids parameter can be added to the YAML configuration to act as a global filter during foundation model training, prior to training and validation splits.
The sameentity_ids parameter is now available in TrainingParams and TestingParams to control entity selection during specific scenario training or testing runs.
[BREAKING CHANGE]: the previously used entities_ids_subquery parameter has been removed.
Refactoring of feature loading pipeline
Improvements to ensure stability, clarity of error messages and to prevent accidental misconfiguration when training scenario models or two-stage pretraining.
[BREAKING CHANGE]: The parameters use_recency_sketches and use_last_basket_sketches are no longer read from data_params block in YAML configuration file and must be passed directly to the pretrain() or train_foundation_model functions.
[BREAKING CHANGE]: the features_path can no longer be modified at scenario training stage, to eliminate the risk of misconfiguration. The path is now always derived from the checkpoint path so that features are in predictable location in the checkpoint.
[BREAKING CHANGE]: the data_loading_params block has been removed from the configuration file.
The following parameters must now be defined as follows:
limit_entity_num_events → limit_entity_num_eventsunder the data_params block
cache_dir → cache_pathunder the data_params block.
Serialization is now enabled for all modalities.
Enhanced BiqQuery connector(minimum viable feature)
Users can now specify a different project as computation engine and a different one as data location.
Standardised Dependency Error Messages
All optional dependency checks generate standardized error messages with clear instruction to install missing packages.
New features - GUI Application (Snowflake Native):
Minor UI fixes only.
Fixes:
Fixed an error when validation set starting date could be picked in a way that resulted with empty history.
Fixed an error when the starting date of testing set could be treated as history.
Fixed an error when a column with less than two values overridden to the categorical type was causing preprocessing stage to fail.
Fixed an error when interpretability returned duplicated modality names.
Fixed an error where classification tasks required thresholds even when the user did not expect predicted labels and output type was set to SEMANTIC.
Updated packages to improve performance, improve security and for compatibility with the latest features.
The two components of the pretrain stage — data preprocessing & representation fitting, and FM training — can now be run independently, or together, as before:
Two respective functions: fit_behavioral_representation and train_foundation_model were added to compute features and train foundation model separately.
Changed monad.pretrain command name to monad.run with optional --fit and --fm arguments to compute only features or foundation model.
Flexible prediction outputs
Introduced different types of predicted output defined by mandatoryoutput_type parameter. This requires changes to previously written scripts.
Added functions readout_sketch and read_target_entity_ids to map recommendation outputs to feature values.
Enhanced model training with early stopping
Introduced early_stopping parameter to prevent overfitting.
Expanded model interpretability
Introduced interpret_entity function to compute event-level attributions for a single main entity.
Automated model testing
Introduced test method to compute metrics based on predictions and ground truth.
Flexible BigQuery connection
Added project_id parameter to define project different from the one in the service account.
New features - GUI Application (Snowflake Native):
Cascading run execution – enables dependent jobs/runs to trigger in sequence.
Run and job status tracking – added detailed status tracking for better monitoring.
New table designs – updated tables with improved layout and readability.
Validation improvements – enhanced input and data validation across the platform.
Multi-GPU training – enabled distributed model training across multiple GPUs.
Listing state restoration – automatically restores UI state when returning to listings.
Fixes:
Fixed an issue where all main entities were loaded on one GPU when distributed training across multiple GPUs was used.
Fixed an issue where default multiclass metric was returning an error.
Fixed an issue where interpretability attributions for time-series features were empty.
Enhancements to Foundation Model training
Improvements to the foundation model training process, leading to better performance in downstream applications.
Simplified configuration by removing the need to predefine certain time-based parameters incl.check_target_for_next_n_days parameter. This requires changes to previously written configuration files.
Optimized training with an updated optimizer that eliminates the need for manual learning rate scheduling.
Improved feature representation through automated parameter tuning.
Performance gains from optimized data handling and more efficient training strategies.
Improved Parquet File Support
Faster and more scalable data processing with an upgraded backend engine.
Enhanced memory management and stability for large-scale data.
Expanded support for advanced query operations on parquet data sources.
Interpretability
Added support for event-level interpretability in classification and regression models.
Fixes
Fixed an issue where the _FINISHED flag for column fit tasks was occasionally set incorrectly, resulting in an unstable resume option.
Fixed an issue where the log in main.log file was empty, incomplete or incorrectly written during model training.
Fixed an issue where `next_n_hours' parameter used in target function was excluding starting time stamp.
Updated packages to improve performance, improve security and for compatibility with the latest features.
Improved handling of time series (BETA)
Users can now enable improved handling of time series by declaring selected numeric columns as time-series. This feature provides superior representation of event sequences and intervals.
Automated sanitization and qualification of column names inwhere_condition
The resolve() function function can now be used in where_condition to enhance consistency and reduce the risk of errors.
Optimised memory utilization for datasources in parquet file format
More stable handling of parquet files used as data sources, including filtering data at an early stage and reading parquet files in chunks to reduce peak memory usage.
Enhanced history / future splitting
Additional sampling strategy ("existing") supports more modeling scenarios, such as basket context for next purchase prediction. Regular timestamps are now used for split points instead of day timestamps.\
Enhanced interpretability of time-based features
Provides deeper insight on the impact of time-based features by separating out periodical counts, sums, and means.
Event aggregations without grouping:
Users can now perform aggregation operations such as sum(), count(), mean(), min() and max() in the target function without needing to group events.
Capping number of CPU resources at fit stage
Users can now limit the utilization of computation resources during the fit stage with the num_cpus argument.
Fixes
Fixed an issue where certain custom metrics were not automatically cast to the appropriate data type.
Fixed an issue where certain features were not saved after a pretraining failure.
Fixed an issue where the most frequently interacting entities could be partially ignored when calculating validation metrics in recommendation tasks.
Fixed an issue where repeating column names across joined data sources might result in conflict.
Fixed an issue where the percentage of NaN values was incorrectly calculated for columns containing both NaN values and empty strings.
Fixed an issue where the CPU cap set with the num_cpus argument was ignored.
Fixed an issue where a .csv suffix was expected instead of .tsv for the predictions file.
Fixed an issue where a file lock set during event grouping resulted in a FileExistsError in case of slow storage.
Fixed an issue where interpret() resulted in an error for data sources with shared entities.
Fixed an issue where interpret() resulted in an error in case of empty quantiles for groups with no events.
Updated packages to improve performance, security, and compatibility with the latest features.
Grouped Decimal Features in Interpretability
Introduced the ability to handle and analyze grouped decimal features, enhancing model interpretability.
Event Attributions to interpret recommendation models
Users can now trace back and understand how specific events influence model outputs and predictions.
Prediction Storage in Snowflake Database
Added functionality to save predictions directly into a Snowflake database.
Data Source Name in Minimum Group Size Logs
Added logging of the data source name when enforcing minimum group size requirements.
Join Functionality for Attribute Data Sources (enhanced)
Expanded support to allow joining attribute data sources with multiple data sources.
Filtering on Extra Columns in Data Source Definition
Users can now filter, group, and leverage extra columns passed in the data source definition.
New Parameter inDataParams:training_end_date
Introduced the training_end_date parameter, providing more flexibility and control over model training timelines.
New Parameters inTestingParams: local_save_location,remote_save_location
Introduced local_save_location and remote_save_location as parameters within TestingParams.
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Note
Please adapt your configuration file to reflect this syntax change.
Extended Group Max Retries
Default values of group computation retries and retry interval have been increased. Default forGROUPS_N_RETRIES is now set to 20 and default for GROUPS_RETRY_INTERVAL is now set to 60. This reduces the likelihood of failures due to transient issues and improves overall robustness. For more information refer to Dividing event tables section.
Entity Number Limit for Target Function Validation
The number of entities that can be used when validating target functions is now capped to ensure efficiency and prevent overload during the validation process.
Enhanced Debug Messages for Target Function Validation
More comprehensive debug messages have been added during target function validation to assist in troubleshooting and increase transparency in the validation process.
Fixes
Fixed issues with None values in grouping.
Fixed regression loss calculation and logging.
Fixed errors in pandas query parsing.
Improved Neptune alerter logging.
Removed unused validations and loss functions.
Optimized memory usage in interpretability.
Fixed handling of missing metrics in Neptune.
Reduced memory consumption.
Improved directory creation based on cache path.
Enhanced schema selection in Hive builder.
Handled potential NaN values in decimal calculations.
Docs
Updated the documentation navigation to be more readable and user-friendly.
Added Recipes section for easy reference when building target functions.
