The platform for building AI from enterprise data using SQL and virtual AI Tables.
MindsDB is an open-source platform that enables developers to build, train, and deploy machine learning models directly within their existing data infrastructure using SQL. By abstracting machine learning models as 'AI Tables,' MindsDB allows for seamless integration of predictive analytics and generative AI into applications without moving data to separate ML environments. In the 2026 landscape, MindsDB serves as a critical orchestration layer between 100+ data sources (PostgreSQL, Snowflake, MongoDB) and AI frameworks (OpenAI, Hugging Face, Anthropic). Its technical architecture leverages an 'AI Logic Engine' that automates data preprocessing, feature engineering, and model selection. This 'AI-in-the-Database' approach significantly reduces the latency typically associated with inference pipelines and simplifies the maintenance of production-grade AI systems. For enterprise users, MindsDB provides a managed cloud environment that handles scaling, security, and real-time data streaming via Kafka or Kinesis, positioning it as the industry standard for data-centric AI development.
MindsDB is an open-source platform that enables developers to build, train, and deploy machine learning models directly within their existing data infrastructure using SQL.
Explore all tools that specialize in build predictive models. This domain focus ensures MindsDB delivers optimized results for this specific requirement.
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Virtual tables that represent a trained ML model, allowing standard SELECT JOIN queries to trigger inference.
Automatic detection of data types, normalization, and encoding for diverse datasets during training.
Native SQL integration with OpenAI, Llama 3, and Anthropic for prompt management and fine-tuning.
Optimized algorithms for handling multivariate time-series forecasting within the database.
Integration with Kafka and Redpanda to perform inference on data streams in real-time.
Provides metadata on feature importance and confidence scores for every prediction.
Ability to toggle between XGBoost, LightGBM, PyTorch, and various LLMs using a single interface.
Install MindsDB via Docker or create an account on MindsDB Cloud.
Connect your primary data source (e.g., CREATE DATABASE my_db WITH ENGINE = 'postgres'...).
Connect your AI engine of choice (e.g., CREATE ENGINE openai_engine USING engine = 'openai'...).
Define a model as a virtual table (CREATE MODEL my_model FROM my_db...).
Specify the target attribute to predict or the prompt for LLMs.
Train the model using historical data using standard SQL syntax.
Verify model status and accuracy metrics via DESCRIBE MODEL.
Perform real-time inference using SELECT statements against the AI Table.
Join AI Tables with production tables to generate augmented data views.
Set up Jobs to automate model retraining or periodic inference.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its innovative SQL-centric approach and ease of deployment, though some users find the debugging of complex SQL errors challenging."
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