Create better AI models with our intuitive Graph UX

Graph UX is an intuitive interface to visualize the end-to-end machine learning (ML) workflow as graphs. This graph ML framework is designed to provide a clear understanding of the modeling workflow from building to evaluating machine learning models.

Modern day ML is all about neural networks, which are easily represented as graphs. This analogy works throughout the machine learning development process, from data collection, model building, model training, all the way to model evaluation and deployment on device. Putting these parts together into one coherent view makes it easier to design your own Edge AI models.

Collect and annotate High Quality Data

Are you tired of the mundane process of collecting data from your embedded devices and sensors, spending days fiddling with how to capture the data with good quality, and then spending even more time annotating and cleaning all of that data?

With DEEPCRAFT™ Studio you get several tools for collecting high quality data straight from any hardware or sensor, either wirelessly or by cable. Capture data with your phone out in the field or straight to any computer or platform running Python. Once the data is collected, DEEPCRAFT™ Studio verifies that all your data is consistent and error free.

Manage, analyze and process your data

Once data is collected, it must be presented to the model in the right way for the model to "learn" i.e. tune its trainable parameters to match the training data - without overfitting, so that it also performs excellent on data it has never encountered before. 

DEEPCRAFT™ Studio has a well-designed, easy-to-use workflow for managing all collected data into different datasets to get you lined up for model building and training. Are you an experienced user? Then you might wonder if you can control exactly which samples goes to which dataset? Well, of course you can! 

Build and train great models

A great machine learning engineer knows the importance of running quick experiments. What this means is, training different models, evaluate the results, adjust accordingly and do it again. This used to be a tedious process involving a lot of intuition gained from thousands of previous experiments, plus the privilege of having access to massive, raw, compute power. The winners are those who have access to the best engineers and the most compute power.

With DEEPCRAFT™ Studio’s AutoML functionality you get the intuition from our experience ML engineers built-in. It generates high performance AI models according to your data, automatically, and these models are optimized for speed and low footprint before deployment. Even better, your generated models are fully transparent, so you can view, edit, or delete them however you like.

Evaluate and find the best model, before deploying

Normally, when building AI models for embedded devices you would have to deploy them on the device so that live testing can be performed. This is time consuming and frustrating. Especially early on in a project where new data is collected and new models are built at a rapid pace.

DEEPCRAFT™ Studio solves this problem by outputting the model predictions on the same time-line as your data. This allows you to see exactly how the AI model interpret all your data, in real time. You can even play it back. This gives you a clear picture of how the AI model will perform live out in the field, before even deploying it. The biggest benefit of this is that you can wait with deployment and live tests until you have a good model. Saving a lot of time.

Evaluation functionality includes

• Understand model performance without any required AI/ML knowledge
• Track model predictions in real time on all your data
• Playback your data and model predictions to see how it will perform live
• Visualize model performance metrics (model size, accuracy, false-positive-rate, F1 score and more)
• Measure the delay between input and prediction

Package your AI models at the click of a button

Packaging an AI model for an embedded device can take months. A firmware engineer needs to port the model to an Edge AI framework. Most of the time only the model and not the data preprocessing will be automatically translated. Once the model is translated it requires testing and optimization, requiring expert knowledge. 

With DEEPCRAFT™ Studio, this step is done with the click of a button. In just a matter of seconds your AI model is optimized, verified and packaged.