In this product news focused blog post, we are happy to bring you features for working with your model off platform, monitor the results of your deployed models and sharing your models with others. Enjoy!
Monitor, download and share your models
Updated model download feature for tf.savedModel format
Deploy and serve your platform-built models off platform with our model download option that is now available in tf.savedModel format. The format includes all the pre- and post-processing done by the platform, and is compatible with TensorFlow 2.5.x.
If you are interested in working with your models off platform, you will be happy to hear that we have created a guide that describes how to download your model as well as provides utility functions to make it easier to work with your models outside the Peltarion platform. You can see how to deploy a SavedModel in these selected frameworks and platforms: ML Flow, TF Serving, UIPath.
You can find the guide here.
Added monitoring capabilities to deployed models
Following the afterlife of your deployed models is a way of making sure that your model is continuously performing well and is also a way of checking that you have made the right decisions when shaping your model, if you need to tune it or potentially replace it.
From now on, whenever you want to monitor API calls, predictions and errors of your live platform deployments, you can do so by visiting the deployments section of the platform and selecting "Monitoring" in the upper left corner.
02/ More on Product development
Visualize and manage your outliers on platform
Bringing you multi-label classification and our new Tune experiment feature
Bringing you our new modeling engine - Okra - and a new datasets view
Our data-cleanser tool is now available on platform
Data cleansing, Multiple output feature selection, and Bubble connector coming your way