This document contains copyrighted and proprietary information of SparkCognition and is protected
by United States copyright laws and international treaty provisions. No part of this publication may
be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic,
mechanical, photocopying, recording, scanning or otherwise, except as permitted under such laws or
with the prior written permission of SparkCognition, Inc.
SparkCognitionTM, the sparkcognition logo, DarwinTM, DeepArmor®, DeepNLPTM, MindFabric®,
SparkSecure®, and SparkPredictTM are trademarks of SparkCognition, Inc. and/or its affiliates
and may not be used without written permission. All other trademarks are the property of their respective
©SparkCognition, Inc. 2017-2019. All rights reserved.
The documentation for this version of Darwin includes:
• The Darwin Release Notes, version 2.0.2
• The Darwin User Interface Guide, version 2.0.2
• The Darwin API User Guide, version 1.34.1
• The Darwin Python SDK User Guide, version 1.44.0
All of these documents are available for download from the Darwin support portal.
Darwin release 2.0.2 is focused on fixing issues and improving the overall user experience. Version 2.0.1
was an internal use only release that did not have any end-user functionality.
New Features introduced in version 2.0 and 2.0.2
• New data analysis and recommendation service providing cleaning and analysis of datasets as they
are uploaded as well as a guided UI workflow for creating models
• New Forecasting Models accessible through the SDK
• Manual and Automatic Train/Validation splitting
• Overfitting improvements
• Support links and a feedback form available from the UI
• Improved UI error messaging
• UTC time is used for SDK/API; local time is used for UI
• (New in 2.0.2) Multi-select of datasets in the Data Bank
• (New in 2.0.2) Improvements to the Data Explorer functionality
• (New in 2.0.2) Improvements to data ingestion to detect malformed .csv files
Fixed Issues in 2.0.2
• Fixed an issue where display population was not working for on-premise instances
• The Japanese version no longer has timestamps displayed in English
• Fixed an issue where columns were excluded for too many categories even though it was changed to
• Fixed an issue where a low variance warning should be a no variance display
• Fixed an issue where cleaning a dataset with no target column produced an error
• Fixed a scrolling issue in the Data Viewer
Known Issues in 2.0.2
• Models created in version 1.x are not compatible with version 2.0.x. Those models will display an
error similar to the following and will need to be re-created.
• The maximum number of features that can be displayed in the UI is 350. For best usability, ensure
that your datasets have less than 350 features.
• Date/Time columns will be displayed incorrectly in the Model Results page. In these cases, the date
columns will be shown as categorical.
• The Mean Squared Error (MSE) loss value presented in the UI is the scaled MSE loss, whereas the
MSE presented in the Model Results view is the unscaled MSE loss.
• When uploading a dataset, ensure that you wait until the upload is complete. Other UI functionality
may not be available while the dataset upload is in progress.
• When downloading large datasets, you are not notified until the download is complete. Do not log
out of Darwin until the download is complete. (Download times may vary depending on bandwidth.)
• Occasionally, the Loss and Algorithm are not populated in the UI. This does not necessarily mean
that the model failed to build, and upon clicking the model card, you should still see the training
performance results and be able to run the model to produce predictions.
• When downloading an artifact using the Runtime Engine (RTE), it is not being downloaded to the
user-defined path. The RTE is saving the artifact in a temporary folder on the local machine. The
download confirmation will output the temp folder path.
• When exporting a model, the ONNX format is only available for neural network models. The JSON
format is available for all model downloads, including neural network models.
• The Darwin RTE does not support unsupervised models nor models with TCN architectures. It only
supports supervised and NBM models.
• Analyze predictions is not supported for large datasets (> 500 MB).
• Re-training or resuming training on a model should be done with the original dataset, since a
different dataset may not have the same categories for each feature as the original dataset.
• Any created models can only specify either zero or a single Target column.
• Because Darwin cannot one hot encode categorical columns with more than max_unique_values in
training and test sets (set to 30), these columns are dropped in test and training sets.
• Darwin only drops duplicated columns in data sets with less than 5000 rows.
• Users must now set recurrent = True with the SDK and API in order to see the LSTM and TCN
• The Darwin UI may not show data when header names contain more than 63 characters. The model
building will operate as expected, but components of the visualization may not appear as designed.
• Odd behavior might be encountered if you try to predict categories on a numeric target or predict
values on a categorical target. Ensure that the type of prediction you want is in alignment with the
type of target.
• Any data set can only have a single (one) date time column or be indexed by date/time, otherwise, an
error message is returned.
The following methods enable you to research issues, create a support ticket, or contact SparkCognition:
• Use the Darwin support portal - Read Frequently Asked Questions (FAQ), download documentation,
or log your issue.
• Email Support - Send email to email@example.com.
• Phone Support - The SparkCognition support line is +1-512-400-2001.
Version | Date
v 1.6 | 01.16.2019
v 1.6.1 | 02.06.2019
v 1.6.2 | 03.25.2019
v 1.7 | 05.16.2019
v 2.0 | 07.29.2019
v 2.0.1 | Internal use only
v 2.0.2 | 09.04.2019