Foundation Models (e.g. GPT-4, Claude 2, Gemini, Llama 2, Mixtral 8x7B) are the cornerstone of modern AI. They learn from large amounts of data and form relationships between the data so that they can be retrieved and explained (see What Is Retrieval Augmented Generation, or RAG?). The data, and relationships between them, are preserved via Embeddings (see What are Vector Embeddings?) for quick and efficient processing.
By converting Consumer Behavior datasets into Embeddings (to be used as features for ML models), Yobi preserves relationships and correlations between consumer behavior, providing the necessary signals about commercial intent while preserving user privacy.
Learn from Data + AI innovators from Twitter, Spotify, Meta, Amazon, and Uber
Interested in learning more about using Pre-Trained Embeddings as Features for ML and how they’re used for AI/ML use cases like instant personalization, improving online conversions, and churn/fraud prevention?
This whitepaper was written to address the most frequent questions customers ask Yobi about using Embeddings to enrich customer profiles.
Download this whitepaper to answer questions like:
1. What is an Embedding?
2. Can Embeddings be more useful than raw data?
3. Can embeddings be private, by design?
Note for Databricks Customers
Databricks customers can use Yobi’s Pre-Trained Embeddings as Features for ML today in the Databricks Marketplace.
They can match their data to Yobi’s Foundation Model in privacy-safe data clean rooms, then request access via Delta Share, without replicating data or building data pipelines (see sample notebooks to explore use cases).