Multimodal Learning
Multimodal machine learning simultaneously analyzes multiple modes or forms of data to gain a more holistic understanding, improving performance on a range of machine learning tasks. Multimodal models enabled Facebook to improve search personalization by producing catalog categorizations for four times as many Facebook Shops. Improvements in search and recommendation performance can enable eCommerce retailers to increase conversion rates by 40%. The advantages of moving to advanced AI such as multimodal are clear but most businesses are still adopting or implementing AI, not optimizing.
Vody’s multimodal optimization tool enables data science teams to quickly and effectively reap the benefits of multimodal models. Utilizing machine learning, the optimal way to fuse the business’s multiple data forms together is identified based on the tasks the data science team wants to perform. The fused representation of the multi-modal data is available through a simple API call, reducing the time to adoption and accelerating the velocity of the data science team.
Language Model
A configurable state-of-the-art language model to understanding words within the context of others words.
Vision Model
A configurable state-of-the-art vision model to understand the images within context.
Structured Model
A transformer style model to understand structured data fields within the context of other structured data.
Fusion Model
A unique model to combine the three data-type understanding into one unified understanding that interprets each component within the context of the others.