PostgresML is an extension for the PostgreSQL database server that enables end-to-end machine learning inside your database. It allows users to build, train, and deploy ML models directly within PostgreSQL, eliminating the need for data movement between systems. By using SQL queries, users can perform training and inference on both text and tabular data, maximizing data privacy and security while reducing latency and improving performance.
PostgresML Core Features
In-database machine learning
SQL-based model training
Inference on text and tabular data
Integrated data security
No data movement required
PostgresML Pro & Cons
The Cons
Does not currently support direct integration with some remote LLM providers like OpenAI
Self-hosting might require Docker and PostgreSQL knowledge
Primarily designed for users familiar with PostgreSQL and SQL
The Pros
In-database ML and AI operations eliminate the need to move data
Supports GPU acceleration for faster computations
Integration with state-of-the-art large language models via Hugging Face
Built-in Pipeline for Retrieval-Augmented Generation (RAG)
High scalability and support for millions of transactions per second
Wide range of supported ML algorithms and NLP tasks
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