Bodyguard

The Bodyguard infrastructure is made up of three Public Cloud instances:

• one for managing the databases
• another for the technology and machine learning models
• another for the systems that keep the mobile application running, by gathering the comments and analysing them using the technology

And to create backups, Charles uses another service from the OVHcloud Public Cloud: Cloud Archive (currently available in France only). He can use it for long-term data storage at a lower cost, but still guarantee security and data recovery.

The Challenge

The technology needed to be capable of analysing the context in which a comment is made, and determining the person or people it is aimed at.

Bodyguard technology had to be able to understand and interpret states of mind. For this reason, an artificial intelligence layer was absolutely vital to reduce false positives (comments detected as hateful when they are not), and increase accuracy.

The solution :

A managed, easy-to-use service that could accelerate the production phase.

Charles chose to use OVHcloud AutoML, a distributed and scalable machine learning platform. With this Software-as-a-Service (SaaS) solution, he could automate the creation and deployment processes, as well as the process for requesting machine learning models. He could also use it to integrate open-source algorithms, such as those offered by scikit-learn.

The result

Charles spent two years developing the final machine learning algorithm and integrating it into a free mobile application, which has been available on Android and iOS since October 2017. Today, Bodyguard deletes hateful comments in real time on YouTube, Instagram, Twitter, Twitch and Mixer.In July 2019, this virtual bodyguard gathered a following of more than 40,000 users, and boasted a satisfaction score of 97%. Here are a few reasons why it is so successful:

•90% of hateful comments detected by the application

•2% error margin (false positives)

•2 million+ hateful comme1300px200

nts deleted in 20 months