Machine Learning Use Case
Topic Classification
Topic classification automates the labor-intesive process of manually categorizing documents. You can use topic classification to label customer support tickets, tag content, detect spam/toxic messages, sort internal files, and more. Learn how to build an end-to-end machine learning pipeline to automatically sort news titles and descriptions into different categories with Predibase and Ludwig.
The Predibase Solution
Boost efficiency with ML-powered topic classification
- Rapidly preprocess unstructured text and tabular features
- Finely tune model parameters such as using pre-trained encoders like Bert with little-to-no-code
- Automate the classification of content into different categories to reduce the manual sorting effort
Train your first topic classification model in < 20 lines of a config file
At the core of Predibase 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
![Read the Tutorial]()
Read the Tutorial
Read this deep dive tutorial to learn how to build an end-to-end deep learning pipeline for topic classification.
![Run the Notebook]()
Run the Notebook
Get started with topic classification using this open-source Ludwig notebook including sample code and data.
![Visit the Resource Center]()
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.
