for ML evaluation & debugging

Explore ML models to acquire better performance and make risks under control

Structured and detailed comprehension of ML models brings you a new experience of data science to get better outcomes efficiently.
Adansons supports AI teams to understand ML models and optimize the performance of ML system.


Less explainability makes ML-team blind

Nowadays it is easy to train ML models, but it is difficult to optimize the performance with limited resources.
Especially with unstructured data such as images, there is no better way to interpret the model's behavior globally, so we tend to rely only on evaluation based on aggregate metrics, such as accuracy. As a result, it is impossible to identify the cause of a model's poor performance. And finally, you will collect data too much in the dark or train models with unnecessarily large parameters.
In addition, overlooking biases and hotspots, data segments with poor performance, are AI teams' burden because they will cause future financial loss and damage your brand reputation.


ML Test & Debug Platform


1. Explainability & Risk Assessment

After training models, we analyze the output of models using metadata to automatically reveal the risky data segments (hotspots).
You can easily interpret the data segment and respond to their risks.

2. Data-centric Pipeline

It is very difficult to learn the best-performing model for all of a dataset.
We provide you with a way to combine optimized models for each segment of data to maximize the performance of ML systems at a minimal cost.

3. Monitoring & Alert

We track model input and output data and alert anomalies in your ML systems to help you maintain their performance and fairness even if the data is changing.
It keeps preventing you from compliance risk.

Use Cases


#Self-Driving Car, #Factory Automation, #Insurance, #Finance, #Healthcare, #CV, #Tabular
Understand model behaviors globally and make models more explainable and trustable using our metadata-based explanation.
You can reveal the risky data segments, and describes them as conditions on metadata values to control programmatically.

Accuracy Optimization

#Self-Driving Car, #Factory Automation, #Finance, #CV, #Tabular
Diagnose the causes of hotspots and improve model performance efficiently by interpreting the metadata conditions of the performance hotspots.
Our data-centric pipeline, which validates each data point based on metadata and combines models or non-ML systems optimized for each segment of the dataset, enables you to maximize the performance of ML systems.

Fairness & Bias Check

#Insurance, #Finance, #Healthcare, #CV, #Tabular
Reveal biases hidden in models with metadata to build reliable models even in unstructured data projects that are difficult to evaluate biases with existing methods.
You can mitigate the biases proactively by adjusting the threshold for the model output or combining other models to correct them.

Performance Monitoring

#Self-Driving Car, #Factory Automation, #Healthcare, #CV, #Tabular
Maintain the performance and fairness of models in the change of data distribution under operation to prevent you from damaging your brand reputation and financial loss.
Our data-centric pipeline tracks and validates the input/output data of models to notify you of anomalies in the ML systems and the best time when AI teams should re-train models.

Strategic Dev & Cost Saving

#Self-Driving Car, #Factory Automation, #Healthcare, #CV, #Tabular
Strategize to optimize model performance, collect data and manage risks of future incidents to make your ML projects more economical and sustainable by understanding models globally.
Combining multiple models with small parameters optimized for each segment of data works even in environments with limited computational resources.


Explore your ML model today


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