Design Trustworthy AI Systems Faster with Saimple, The Leading Explainability and Robustness Validation Tool

Saimple is the only software on the market capable of providing unique insights that can help data scientists and AI engineers improve their models.

Streamline Your Neural Network Design Process with Formal Methods

The traditional approach to designing neural networks often involves a time-consuming and frustrating trial-and-error process. But with Saimple’s formal methods, you can ensure the success of your neural network with greater efficiency and confidence. These tools allow you to move away from unreliable empirical design and minimize the need for tedious experimentation. As a result, you can enjoy a streamlined and more effective design process that yields better-performing neural networks.

Step-by-Step Audit and Optimization

Saimple allows you to tune your models’ hyperparameters with better metrics, including:

Continuous measurement of stability variations
Visualization of input impact for each layer
Robustness evolution checks on different noises

By understanding the impact of each design change, you can find the right model. With Saimple, you can compare metrics before and after a change, and even include Saimple metrics in your fitness function to guide your search.

Improve your neural network training

Neural network training is important because it allows a machine learning model to learn and make predictions or decisions based on input data. By optimizing the model through training, it can accurately identify patterns and relationships within the data, leading to improved performance and predictions.

Improve your training set

With Saimple’s formal analysis, you can construct direct correlations between decisions and inputs. This enables you to:

Identify features that make the training biased
Orient your adversarial attacks generation
Drive the iterative reinforcement of your training set

Perform only the necessary data augmentation

To ensure that your neural network or SVM performance and reliability are carefully controlled and validated for production, Saimple can help you:

Check stability boosts
Check reinforced features after each augmentation
Tune your augmentations through formal metrics
Stop the augmentation process at the right time

Validate your neural network

AI adoption is about the trade-off between risk and reward of its use, and for this, validation is crucial.

Saimple can help you validate your neural network’s robustness for real-world applications. This includes:

Modeling classical or environmental noises
Validating against a domain of use, rather than isolated inputs
Validating against specific noises that you define
Detecting robustness regression early on
Implementing unit tests for decision explainability

Automate the validation and documentation process

Automation is key to boost validation.

Use Saimple for batch testing using its API (on premise / private cloud or SaaS)
Adapt the Saimple’s footprint to your hardware resources (number of threads, cores, RAM, etc.)
Seamlessly include Saimple metrics in your continuous integration
Generate audit report automatically