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Overview: 

To automatically provide explanations on complex machine learning models is not an wasy task

The goal: 

To automatically provide explanations on complex machine learning models is not an wasy task

The challenge: 

The final result is state-of-the-art methods used for explaining deep neural networks available at Lemonade, thus enabling users of Lemonade to easily equip complex machine learning models with explanations.

The Impact: 

Scientist can easily deploy their applications over virtual clusters without the burden of installing and configuring the resources, and without the need of managing the infrastructure resources and being able of accessing hybrid resources connected in a secure way.

Without atmosphere: 

Users had to manually implement explainability methods and integrate them into the target model.

With atmosphere: 

Users can easily pick the explainability method of interest in Lemonade.

WHO BENEFITS & HOW?

Application Developer

Can gain a better understand of the machine learning sytem.

Data Scientist

Can use the explanations to fine tune and debug the machine learning model.

Application Manager

Can gain a better understand of the machine learning sytem.

System Administrator

Can gain a better understand of the machine learning sytem.

Data Owner

Can gain a better understanding about how the data is being used by the machine learning model.

Literature: 

Not relevant