April 26, 2023
- Discover changes — In preparation for bigger upcoming changes to some of our AI asset types, we updated the design of Discover to make finding the assets that you are looking for easier and more intuitive! The changes will also be reflected on your assets that are accessible from your Dashboard.
- Introducing a new asset type: Corpus — In this release we added a new asset type that you can find, onboard, and use in aiXplain. The corpus is structured data which is not defined to be used for a single particular AI function. There are limited things that you can do with corpora for the time being but our next release will allow extracting multiple datasets for multiple AI functions from a single corpus. For example, I can onboard a corpus with columns for speech, transcript, translation, speaker age, speaker gender, speaker ID where I can use it to create datasets for Speech Recognition, Speaker Diarization, Speech Classification, and Translation.
- Updates to API access keys — We previously added the ability for teams to generate access keys from the team settings. In this release, we removed the auto-generated default access key which was specific to a particular model or pipeline for each team. This change allows creating and using different access keys for different endpoints. With added security allowing members to delete and recreate access keys as they see fit.
- Translation metrics added in Design — We added all of the Translation metrics found in Benchmark to the metric node inside Design.
- Improved Benchmark — We improved the way that scoring in Benchmark takes place. Those changes will have positive impact on Benchmark performance and stability.
- Enhanced network security — We added improved encryption to the tokens sent out over the network.
- User guide in Dashboard — Added a user guide in the Dashboard to help the members access the documents page and use the platform.
- Fixed a bug that made Benchmark reports appear to be stuck at 90% when they are complete.
- Fixed a bug where members couldn’t find the “switch team” option when the team invitation is accepted.
- Fixed a bug that prevented the phone number verification to appear on the dashboard.
- Fixed a bug in displaying metrics inside the metric node in Design that collapsed the scroll.
- Fixed a bug where some function names were displayed incorrectly.
February 8, 2023
- Phone verification — Go to your account settings and verify your phone number to get access to your free credits!
- Renaming pipelines — Added the ability to rename pipelines after creating them.
- Fixed a bug where clicking on canvas doesn’t close dropdown menus in Design.
- Fixed a bug where the back button wasn’t working as intended in some areas of the app.
- Fixed a bug where you were redirected to the wrong URL after payment method is updated.
January 12, 2023
- FineTune tool — We have launched the ability to fine-tune select models for MT and ASR. You can also view the logs of the model inside the details page for it. Read more
- Segmentor node and constructor node — We added new nodes into Design! Using the segmentor node and constructor node, members now have the ability to customize the segmentation of the data flowing through their pipelines and reconstruct them back. This optimizes the use of Design for models where the input size matters.
- Metric node — We added another new node in Design! Members can add benchmarking to their pipelines as a node which gives them the ability to evaluate the performance of their models while a pipeline is running.
- Activity logs — We added a log for all your activities on aiXplain which allows you to filter them by product and by status. This will give members more control and insight over what activities are being done on their teams such as model fine-tuning and Benchmark reports.
- Imputation of failed inputs — Now in Benchmark, members have the option to impute the scores of some failed segments to get a higher representation of their data.
- Added the number of segments to be displayed onto the asset card for datasets.
- Added the ability to create API keys on aiXplain: In the top menu under team settings, members will be able to generate API keys which can then be used as a universal key to run all of their assets.
- Removed phone number from registration.
- Added icons next to AutoMode and FineTune models.
- Added multiple notifications in platform for Benchmark, FineTune, and AutoMode.
- Fixed an issue which didn’t allow saving pipelines without setting decision nodes.
- Fixed a bug that showed a redundant message when not having enough credits on the platform.
- Fixed a bug that allowed users to add multiple datasets in benchmarking when they shouldn’t.
- Fixed a bug where submetrics showed redundant metrics name.
Fixed an issue with the IQR filter displaying inside the segments tab in a benchmark report.
- Fixed a bug where the label of the decision node couldn’t be updated.
- Improved error handling for model and pipeline tryout.
November 17, 2022
- The new aiXplain release offers improved system performance and stability with a brand new user-friendly and intuitive design!
- Dashboard — The aiXplain Dashboard is the go-to place for all assets from aiXplain tools.
- Asset drawer — The asset drawer allows the collection of assets in one place where they can be used with any tool on aiXplain.
- Derivative data — Create derivative data through pipelines or Benchmarking reports and view data history.
- Billing nested view — This new view allows for a simpler report of the transactions made on aiXplain at a glance.
- AutoMode — aiXplain’s AutoMode is an ensemble model that routes the input to the most optimal system according to the quality preference that it is trained on it on. The supported functions for AutoMode are Automatic Speech Recognition and Machine Translation.
- Multi-input/output support — Design now supports connecting and running multiple input and output nodes in a single pipeline.
- Subtitling node — The Subtitling node allows having an entire subtitling system in one node.
- Decision node — The decision node allows members to set routing of the data in a pipeline based on set conditions and values.
- Benchmark for Diacritization — Benchmark now supports Arabic text diacritization.
- Benchmark for text classification — Benchmark now supports text classification.
- Categories in dataset creation — Members can now specify categories when they are creating a dataset on aiXplain; these categories will be used when calculating bias in Benchmark jobs.
- Light/dark mode — aiXplain now supports light and dark modes!
- Downloading results as CSV — If a dataset license is owned, members can now download the results of a Benchmark report as a csv. While a Benchmark job is running, you can download the up-until-this-moment-computed results as a csv.
- Zooming in benchmarking report plots — Now members can zoom in the benchmarking report plots.
- Canceling and rerunning models in benchmarking — Now you can cancel and/or rerun a single model in benchmarking. This is useful for when a model fails during benchmarking.
- Data sampling — Members can now specify how the results for Benchmark jobs can be displayed based on the data samples they’d like to use.
- All segments — Shows the results of all the segments in a Benchmark job with a penalty score for failed segments.
- Successful segments — Shows the results only based on the successful segments in a Benchmark job, disregarding failed segments in calculating the scores.
- Intersecting successful segments — Shows the results only based on the common successful segments between all models.
- Interquartile range multiplier — This configuration allows members to specify the multiplier at which the length of the whiskers in box plots is set.
September 27, 2022
- AutoMode as an individual tool — Previously, AutoMode was accessible through Discover and Benchmark. To shed more light on the product it is now its own experience on aiXplain where users can train and deploy their custom AutoMode model through!
- Zooming in Benchmark report plots — Now we can zoom in the benchmarking report plots.
- Downloading intermediate Benchmark results — While a benchmarking report is running, now we can download the up-until-this-moment-computed results as a csv.
- Canceling and rerunning single models in Benchmark — Now you can cancel and/or rerun a single model in Benchmark. This is useful for when a model fails during benchmarking.
- Updates to performance table — Now the performance table shows the completed and failed segments for each supplier.
- Allow dataset upload of larger files — Due to infrastructure limitations, dataset upload would timeout after 5 minutes of dataset upload. This caused issues with trying to upload large datasets to aiXplain. This is now fixed.
- Fixed the colors of the confusion matrix in classification benchmarking to be more representative to a heatmap.
- Fixed a bug with scoring for Diacritization Benchmark which displayed wrong scores at times.
August 31, 2022
- Benchmarking for text classification models — We have expanded our benchmarking capabilities to support text classification models. The current supported functions are Sentiment Analysis and Offensive Language Identification.
- Subtitling node — We added a new node in aiXplain’s designer, the subtitling node.
- AutoMode for ASR — Following AutoMode for MT, you can now create your own AutoMode ASR model from Discover.
- Bring your own models output — We have added the ability for users to upload their models output during the dataset upload and compute the score of that model without the model being onboarded on aiXplain.
- Bring your own bias categories — Similar to bringing your own model’s output, during dataset upload you can identify categorical columns that you can use for bias analysis and topic classification in benchmarking.