PostgresML aims to be the easiest way to gain value from machine learning. Anyone with a basic understanding of SQL should be able to build and deploy models to production, while receiving the benefits of a high performance machine learning platform. PostgresML leverages state of the art algorithms with built in best practices, without having to setup additional infrastructure or learn additional programming languages.

Getting started is as easy as creating a `table` or `view` that holds the training data, and then registering that with PostgresML.

SELECT pgml.create_regression('Red Wine Quality', training_data_table_or_view_name, label_column_name);

And predict novel datapoints:

SELECT pgml.predict('Red Wine Quality', red_wines.*)

FROM pgml.red_wines

LIMIT 3;

quality

0.896432

0.834822

0.954502

(3 rows)

PostgresML similarly supports classification to predict numeric scores rather than classes for novel data.

SELECT pgml.create_classification('Handwritten Digit Classifier', pgml.mnist_training_data, label_column_name);

And predict novel datapoints:

SELECT pgml.predict('Handwritten Digit Classifier', pgml.mnist_test_data.*)

FROM pgml.mnist

LIMIT 1;

digit | likelihood

5 | 0.956432

(1 row)

Checkout the [documentation](https://TODO) to view the full capabilities, including:

- [Creating Training Sets](https://TODO)

- [Classification](https://TODO)

- [Regression](https://TODO)

- [Supported Algorithms](https://TODO)

- [Scikit Learn](https://TODO)

- [XGBoost](https://TODO)

- [Tensorflow](https://TODO)

- [PyTorch](https://TODO)

- Model management dashboard

- Data explorer

- More algorithms and libraries incluiding custom algorithm support

*How well does this scale?*

Petabyte sized Postgres deployements are [documented](https://www.computerworld.com/article/2535825/size-matters--yahoo-claims-2-petabyte-database-is-world-s-biggest--busiest.html) in production since at least 2008, and [recent patches](https://www.2ndquadrant.com/en/blog/postgresql-maximum-table-size/) have enabled working beyond exabyte up to the yotabyte scale. Machine learning models can be horizontally scaled using well tested Postgres replication techniques on top of a mature storage and compute platform.

*How reliable is this system?*

Postgres is widely considered mission critical, and some of the most [reliable](https://www.postgresql.org/docs/current/wal-reliability.html) technology in any modern stack. PostgresML allows an infrastructure organization to leverage pre-existing best practices to deploy machine learning into production with less risk and effort than other systems.

*How good are the models?*

Model quality is often a tradeoff between compute resources and incremental quality improvements. PostgresML allows stakeholders to choose algorithms from several libraries that will provide the most bang for the buck. In addition, PostgresML automatically applies best practices for data cleaning like imputing missing values by default and normalizing data to prevent common problems in production. After quickly enabling 0 to 1 value creation, PostgresML enables further expert iteration with custom data preperation and algorithm implementations. Like most things in life, the ultimate in quality will be a concerted effort of experts working over time, but that shouldn't get in the way of a fast start.

*Is PostgresML fast?*

Colocating the compute with the data inside the database removes one of the most common latency bottlenecks in the ML stack, which is the (de)serialization of data between stores and services across the wire. Modern versions of Postgres also support automatic query parrellization across multiple workers to further minimize latency in large batch workloads. Finally, PostgresML will utilize GPU compute if both the algorithm and hardware support it, although it is currently rare in practice for production databases to have GPUs. Checkout our [benchmarks](https://todo).

7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4 -- features as variadic arguments

) AS score;