Machine Learning Use Case
Bots can run havoc on social media apps, video game platforms, review sites, and more. Detecting these bots to filter unwanted content is critical. Learn how to build an end-to-end machine learning pipeline to automatically detect bots.
The Predibase Solution
Detect bots to protect your users from unwanted content
- Rapidly preprocess tabular, text and image features
- Finely tune model parameters such as using pre-trained encoders like Bert with little-to-no-code
- Automate the detection of twitter bots to improve content moderation
Train your first bot detection model in < 20 lines of a config file
At the core of Predibas is Ludwig, an open-source declarative ML framework, that automates complex model development with a simple configuration file. Predibase builds on these capabilities with a collaborative, easy-to-use, and fully managed ML platform in the cloud.
Resources to Get Started
Watch the Webinar
Deep dive into declarative ML systems and learn how to build an end-to-end ML pipeline for twitter bot detection.
Visit the Resource Center
Visit the Predibase resource center to see our collection of ebooks and webinars on topics from multimodal ML to computer vision.
Read the Guide
Check out our solution guide to learn more about declarative ML and the Predibase platform.