The web service supports generating embeddings from OpenAI and Ollama AI models. It also provides a fallback for users without access to AI models running on a remote server through PostgresML. - Source: dev.to / over 1 year ago

That's outside of the database, though. This is more like what I had in mind -- I just found it: https://postgresml.org/. - Source: Hacker News / about 2 years ago

Some excellent tools were created to represent these tasks "naturally" in SQL and even let most of the computation happen inside the database. PostgresML is a great example. It's built above PostgreSQL and provides a set of functions that allow you to train and use machine learning models with SQL. Here's how you can train a classification model for the classic handwritten digit recognition problem:. - Source: dev.to / over 2 years ago

PostgresML | You know Postgres. Now you know machine learning – PostgresML. - Source: dev.to / over 2 years ago

You can swap in almost any open-source model on Huggingface. HuggingFaceH4/zephyr-7b-beta, Gryphe/MythoMax-L2-13b, teknium/OpenHermes-2.5-Mistral-7B and more.If you haven't seen us here before, we're PostgresML, an open-source MLOps platform built on Postgres. We bring ML to the database rather than the other way around. Source: over 2 years ago

Featureform The success of a machine learning model relies on the quality of data and, hence, the features fed to the model. However, in large organizations, members of one team may not be aware of good features developed by other teams in the organization. A feature store helps eliminate this problem by providing a central repository of features that are accessible to all the teams and individuals within an... - Source: dev.to / almost 2 years ago

Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model's features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the organizational problems:. Source: about 4 years ago

Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn. - Source: dev.to / about 4 years ago