PostgresML: MLops Platform with Fast, Simple, Powerful Models in PostgreSQL

What is PostgresML?

PostgresML is an all-in-one MLops platform delivered as a simple PostgreSQL extension. It enables the creation of fast, straightforward, and robust models directly within your database.

How to use PostgresML?

PostgresML's Core Features

Seamless integration within PostgreSQL

High performance with low latency and computational overhead

Open-source platform featuring diverse ML libraries

Scalable with custom Postgres pooler

Compatibility with popular ML toolkits and models

PostgresML's Use Cases

Chatbots

Site Search Optimization

Fraud Detection

Forecasting

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FAQ from PostgresML

What is PostgresML?

PostgresML is an all-in-one MLops platform delivered as a simple PostgreSQL extension. It enables the creation of fast, straightforward, and robust models directly within your database.

How to use PostgresML?

Using PostgresML is easy. Follow these three steps: 1. Train your model with the pgml.train() function. 2. Deploy your model using the pgml.deploy() function. 3. Make predictions through the pgml.predict() function.

How can I use PostgresML?

Using PostgresML is simple. Train your model, deploy it, and make predictions. Utilize functions like pgml.train(), pgml.deploy(), and pgml.predict() to achieve these tasks.

What are the core features of PostgresML?

PostgresML features seamless integration within PostgreSQL, high performance with low latency, an open-source platform with diverse ML libraries, instant scalability via a custom Postgres pooler, and compatibility with popular ML toolkits and models.

What are the use cases of PostgresML?

PostgresML can be used for various applications such as building chatbots, optimizing site search, detecting fraud, and forecasting time series data.