MindsDB, the world's fastest-growing open-source applied machine learning platform, allows developers and data engineers to quickly and effectively build AI-powered solutions using their existing skills.

Enable state-of-the-art Machine Learning frameworks as a native component of the data layer (database, data lake, data stream, etc). Train ML models as easily as running a single SQL command, and make predictions with the same experience as querying data from a table.

You can choose from 70+ native integrations with the most popular data platforms and ML frameworks, bring your custom models or even build your own integration.

Supported machine learning tasks:
Classification,
Regression,
Time-series (including multivariate models),
Natural Language Processing.

Min Prerequisites: SQL skills, Basic understanding of machine learning concepts.

HOW IT WORKS:
Connect your database to the MindsDB instance using one SQL command, for example:

CREATE DATABASE your_db_name
WITH ENGINE = "db_type",
PARAMETERS = {
"user": "username",
"password": "password",
"host": "1.111.11.111",
"port": "5432",
"database": "demo"
};

Train an ML model from your data (note that columns equate to model features) using a command like the following:

CREATE MODEL
mindsdb.model_name
FROM your_db_name
(SELECT * FROM your_training_table_name)
PREDICT predicted_column_name;

Check the https://docs.mindsdb.com for extended syntax and advanced modeling capabilities.

Once the model status is complete, start making predictions either one by one or in bulk:

method 1 (one by one):

SELECT predicted_column_name
FROM mindsdb.model_name
WHERE condition1='value1', condition2='value2' . . .

method 2 (bulk via JOIN):

SELECT
a.input_column1, a.input_column2
b.model_name
FROM your_db_name.input_table AS a
JOIN model_name AS b;

Please refer to MindsDB community Slack to get support or ask questions from experts
https://mindsdb.com/joincommunity

FAQ:

What can I do with MindsDB?
There are many AI possibilities you can build - like predicting sales, inventory levels, assessing risks, analyzing sentiment, detecting hate speech, etc - you name it. Please check community tutorials available on https://docs.mindsdb.com and online

I'm an experienced data scientist. Can I benefit from MindsDB?
Of course. Bringing ML models to the data layer solves many fundamental machine-learning problems and significantly simplifies ML workflows.

How is my database performance affected?
MindsDB runs on a separate instance and doesn't impact your database performance

How can I fine-tune models?
Please refer to docs for an available syntax or how to import a pre-built model.

What kind of custom-built models can I import?
You can import models via MLFlow, Ray Serve, and also from NLP repositories like Hugging Face, OpenAI, etc.

What support options are available
Support is provided via community Slack https://mindsdb.com/joincommunity. There is also Enterprise Support available on request.

How can I contribute to MindsDB?
Please check community guidelines and incentives at https://mindsdb.com/community