One important part of Picsellia is the ability to store, share, re-use and deploy your trained models, here we will cover this topic.
The whole point of training AI algorithms is to obtain a powerful model that you will need to deploy to solve your problems.
But what you will discover during your many AI projects (or that you already discovered if you are already advanced in the field) is that
you will never deploy only one model and be done with it
Indeed, your models must evolve with the changes that occurs in real-world data over time.
This means that, to be efficient in training and deploying up-to-date models there are several steps that you need to master :
Store and version your models
Be able to reproduce results, meaning you must always know how your model was trained
Deploy in a scalable way
Monitor the model's predictions over time and avoid drift and bias
Share your models with your team/organization
It seems like a lot of work 🥵
Hopefully with Picsellia you are in good hands because we provide you with all the tools needed to perform all those steps seamlessly !
To know more, here are the pages/tutorials you need to check in order to leverage all the features we have developed to support your model development :
For example, our model HUB (and your Organization HUB) allows you to store, document, and share your trained models with all their files with your team 👇
To help you train and evaluate your models properly, you can use our experiment tracking system 👇
Evaluate your models
Deploy your models in only one-click and get your API endpoint 👇
Deploy model in production (Tensorflow only)
Monitor your model predictions and also send them directly to your datasets for further exploitation 👇
Feedback loop - Send predictions from models to Datalake or Datasets
Launch your Hyperparameters tuning