Release notes¶
Version 10.14¶
Studio¶
Features¶
- Object detector : - Migration from YoloV3 to YoloV5 and improvements of object detector uses cases - Target file can now be load for object detector prediction in order to have a prediction score
- Time series : - Rules of time series are now displayed in the interface of use case creation - Derivation and forecast windows fields are now easier to fulfill thanks to auto completion and restriction according to time series rules
- Administration : - CPU, RAM and notebook lifetime can now be defined up by admins
- Versioning : - Models can now be identified as deployable. In the store, the list of deployable models is now the one identified in the studio as deployable
- Reporting : - Report of use case models can be now generated trough the use case page
- Feature importance : - Feature importance of a model are now paged 10 by page - You can also search a feature and sort the features by importance
- DAG : - The DAG of a use case is now more detailed
- Other features : - When new version of a use case is created, the type of training is no longer editable - Feature grades have now an explanation in the interface - In the model page, all feature engineering used by this models are now displayed, even the internal ones
- Bug fixes : - Difference between top bar and use case page about best performance calculation - Bug regarding object detector use cases and the « filename » name column - Train a classification using error_rate_binary metric created an error
Store¶
Features¶
- Object detector : - Object detector use cases can now be deploy from the studio to the store
- Other features : - List of deployable models is now limited to identified models in the studio - Private GIT repositories are now identified with a lock icon
- Bug fixes : - Status display not « running » when a use case is deployed - Fields of a new app versioning where not pre filled when using GitHub
Version 10.13¶
Release date: 2020-10-22
Studio¶
Features¶
- User interface of models selection : Users choices regarding model selection in advanced options during the creation of a use case are now extended.
- FastText : First NLP treatments are coming in Prevision.io platform. Text roles features of a use case can now be optimized using NLP algorithm
- Image Detector use cases : migration to YoloV5 treatment allowing a better treatment for image detector use cases
- UX/UI improvements
Bug fixes¶
- Fix of a display issue regarding number format depending on user interface language
- Lack of some calculated feature grades
- F1 score calculation of a model is now based on the optimal threshold value
- Fix of graphical display regarding bivariate analysis
Store¶
Features¶
- Webapp and notebook versioning : creation of new version of an already existing web app and notebook
- Access to private GitLab repo : SSO connexion to GitLab
- Build and deploy logs are now available for webapps
- application list of allowed user is now editable
Bug fixes¶
- Some field were not typed producing errors when fulfill with wrong type of data
- fix regarding an issue affecting all user of an instance instead of the user selected
Version 10.9¶
Release date: 2020-08-20
- New Prevision.io Studio homepage
- New Prevision.io Studio help page
Version 10.8¶
Release date: 2020-08-06
- SDK: Add versioning & sharing methods
- Add embedded support in store and studio through Zendesk
Version 10.7¶
Release date: 2020-07-23
- Connectors for Google Cloud Platform Buckets and BigQuery
- Advanced analytics for time series datasets
- Public mode for app deployment in store
- Subdomain URL mode for app deployment in store
- Add the capability to define environment variables when deploying apps in stores
Version 10.6¶
Release date: 2020-07-09
- Various improvements for apps deployment in store
- Better handling of very large datasets (> 10k columns)
Version 10.5¶
Release date: 2020-06-25
- Optimizations & bug fixes
- Model and app deployment is now entirely located in the Prevision.io store
Version 10.4¶
Release date: 2020-06-11
- Detailed statistics and analyses for datasets accessible from the data page
Version 10.1¶
Release date: 2020-04-10
- Create a new usecase from an existing one
- Simple models updated in order to match classical model analytics
- R & Python packages updated + new packages availlable for development environment
Version 10.0¶
Release date: 2020-03-05
- New graph-based usecase training monitoring
- Update scheduler page
Version 9.7¶
Release date: 2020-02-20
- Update notebook page to include current CPU & RAM usage
- Update and relocate administration page (now in top-right menu)
- Access data explorer from data screen
Version 9.6¶
Release date: 2020-02-06
- Change and relocate main menu to top bar
- Start usecase from data screen
- Update contact page
Version 9.5¶
Release date: 2019-12-19
- Refactoring of the main dashboard screen
- Refactoring of the usecase screen, including new analytics
- R & Python packages updated to matchs usecase APIs
- Improved explain screen stability when simulating predictions
- Added support of object detection usecase with CPU only (might take some computing time)
- Feature quality estimation
Version 9.4¶
Release date: 2019-10-04
- Refactoring of the data screen
- R & Python packages updated to matchs data screen APIs
- Improved rules of detection of typical columns (ID, TARGET, FOLD, WEIGHT)
- Improved explain screen stability when values are missing
- Improved date columns parsing in a dataset that handles multiple time zones
- Faster prediction time retrival when listing a high number of predictions
- Creation of an open data base with accessible data for special days (holidays, public days, sales, …) and for weather data
Version 9.3¶
Release date: 2019-08-14
- Refactoring of the new use case screen
- R & Python packages updated to matchs new use case APIs
- Refactoring of Prevision.io APIs. Documentation available @ https://xxx.prevision.io/api/documentation (xxx = instance name)
- Creation of instance specific Prevision.io’s store, visible @ https://xxx.prevision.io/store (xxx = instance name)
- Optimisation of training time for gradient boosting trees models