Within our brand new release 11.3 we have added the capability to have multiple pipeline engine with different python versions.

By default, Wizata was supporting Python 3.9 and will now be based by default on Python 3.12. Additionnally special builds with python version 3.11 are also possible.

Check your versions

To check versions currently deployed within your environment navigate to AI Lab and check the runners versions:

If you need other Python versions deployed in your environment please contact our team.

Choose your version - Triggers & API

When using the API, make sure you use a Python version identical to the one of the runners you want to use or you can set manually on an execution the desired version (i.e. execution.version = "3.12" )

Within a trigger or an experiment on UI, you can now select a version. If no version selected, the system will use the default version (3.9 for environment pre-existing 11.3 and 3.12 for newly environment)

Update your solutions

If you have multiple versions, and you would like to update your solution pipelines and scripts, please follow this tutorial Upgrade your solution Python version

We have improved Machine Learning model management within our app with many little improvements but with key new features :

• Models are now versionned generating an alias each a model is trained or upload inside the app. You can set the one you desire as active or app will always take the most recent one.
• A brand new UI page allows you to browse all models, manage them and upload new ones manually.

Quick Action Points

The update is seamless but still minor changes are necessary ; if you already use a train script within a pipelines you need to adapt the context.set_model( ... ) to only pass the model as only required info or add extra parameters as named parameters and they will be stored as extra files.

See Model step for detailed explaination.

To go further

Please learn more about the model management and new principles directly from new documentation section about Models.

We now officially support PyTorch in AI Lab pipelines and training scripts when running on Python 3.11 or higher.

New capabilities include:

• Training and using PyTorch models directly inside Script and Model steps
• Passing and manipulating torch.Tensor objects between pipeline steps
• Storing PyTorch models through context.set_model()
• Models must be saved as full TorchScript .pt objects (scripted modules), ensuring safe, portable, framework-agnostic deployment across all execution environments
• Support for tensor‐based feature extraction, embeddings, neural networks, etc.
• GPU acceleration where available

No additional configuration is required — simply import PyTorch in your pipeline scripts and start building.

Minor change logs for release version 11.3

Improvements

• Add support of pandas.Series as return type of a model
• Optimize docker image built size by refactoring some librairies

Bugs

• Fix an issue with assignment of default values on properties within an experiment
• Fix an issue with update button on Twin type.
• Fix an issue that blocked deletion of a unit attached to a template property
• Fix an issue on group systems queries using some twin hierarchy
• Fix an issue on data explorer using formula data source
• Fix an issue when changing smooth to dense on data explorer
• Fix an issue on business labels endpoints
• Fix an error on OPC writer within new edge modules
• Fix an issue listing twin types on digital twin chart

The new Dynamic Selector allows you to query data based on the digital twin structure and datapoint properties, without the need to manually name or list datapoints.

Starting from v11.2, we have also refactored the Twin entity by introducing a new entity: TwinType. With TwinTypes, you can now define your own types of digital twins, enabling greater customization and better alignment with real-world twin configurations.

Additionally, we’ve introduced Extra Properties on Twin entities, providing more flexibility for storing and using custom metadata in your pipeline logic.

You can check more information in our dedicated article: Dynamic Selector .

Starting v11.2, we have made significant improvements to how categories and units are managed across the platform. In addition to serving as metadata that provide context for your datapoints, you can now assign them directly to template properties and even apply unit conversions within your queries and pipeline solutions.

We have added a wide range of new default categories and units ready to be used in your solutions, while still allowing you to create and customize your own, including defining their respective conversion formulas.

For more information, check our dedicated articles on Categories, units and labels and Dynamic Selector.

We have reworked the time interval selector in the Control Panel for both Grafana and Streamlit components, introducing time selection persistence and override capabilities.

You can now override default relative time, which will remain active within the tab context until manually refreshed or cleared using the X button. A subtle yellow highlight indicates when the override status is active, improving visibility and control over the selected time range.

Additionally, we have added a new auto-refresh capability for components. You will visualize the time selector with a green dot indicating live mode is active.

For more information, you can check our detailed article on Interacting with Time Interval selector.

Changelog for release version 11.2

Improvements

• Add Siemens S7 consumer for Edge data connectivity.
• Clear search box after selecting a tile.
• Remove auto-redirect on twin child panel.
• Add NumPy as default library on script execution.
• Add script name display on execution failure.
• Add eventType tag to group system.
• Support eventType in queries and refactor them.
• Add button to hide alerting conditionnal formatting on control panel.

Bug Fixes

• Fix an issue where navigating on mobile didn't keep the right tab when switching between kilns.
• Fix a bug where filters closed automatically on mobile after selecting one option.
• Fix total filter not applying correctly and always showing first 20 results per page.
• Fix refresh button on Edge Logs not updating timestamps and calling endpoint with same time.
• Fix missing default tiles on control panel without filters active.
• Prevent creation of Edge entities using uppercase IDs.
• Fix a bug when expanding attached datapoints panel on twin view.
• Fix an issue where experiment couldn't be executed on first run due to disabled button.
• Fix yellow error popup appearing incorrectly on twin panel options.
• Fix "Active filters" orange icon showing only for Assets filter and clear button not working.
• Fix orange icon not displaying for other active filters (favorites, twin type, template, etc.)
• Fix pipeline image generation error for Wizata library steps.

We have reworked web mobile display of UI to properly render the control panel on mobile phone. Navigate to your app with your mobile phone browser and start looking at your favorite browser from your mobile device.

We have added a complete support to allow you install, configure and maintain your edge device directly from our web app.

Go to Data Hub >Edge Device . You can from there Register and setup an Edge device, Using Edge to connect your data and Troubleshoot your device.

We have also extended the list of natively supported protocols, don't find the one your are looking for ? Contact our support on [email protected]