Course of Automatic Embeddings in Postgres

💸 First lesson available without registration 💸

If we want to fully leverage the power of embeddings for semantic searches, recommendations, or AI systems, it is important to implement them efficiently and scalably.
But this is not always simple. Many times we wonder:

• ❓ Where do we generate them from?: Database or application?, synchronously or asynchronously?
• 🐌 Poor performance and high costs when generating and updating embeddings.
• ⚙️ Lack of robustness and testability in the embedding generation pipelines.

This causes us to spend more time solving infrastructure problems and less on developing functionalities that add value.

In this course, you will see the different places to generate embeddings, which applies best for each case, and how to make it scalable. All thanks to the potential of Postgres. We will also see the recommended implementation of Supabase and the one generated by Timescale.