Using Multi-Modal ML to Predict Customer Ratings

The blog post details the use of Predibase, a low-code declarative machine learning platform, and Ludwig, an open-source project, to develop and deploy a machine learning model that predicts customer review ratings using a multi-modal dataset. The dataset used comprises over 20,000 anonymized reviews from the Women's E-commerce Clothing sector, featuring diverse data types such as categorical, numerical, and text features. The approach involves establishing baseline models with neural networks and tree-based models, iterating to improve model performance by incorporating unstructured text data, and eventually fine-tuning advanced models like BERT for enhanced results. Predibase streamlines the integration of structured and unstructured data, enabling easy model iteration and operationalization, including deployment through batch or real-time inference. This process highlights the platform's ability to handle complex data efficiently, making it suitable for businesses aiming to leverage machine learning for customer sentiment analysis and other predictive tasks.

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