MindsDB enhances SQL with AI Building Blocks for developers creating AI applications that need to be coupled to realtime data:
AI Tables:One of MindsDB’s innovations is the ability to treat AI (MODELS, AGENTS, KNOWLEDGE BASES) as virtual tables that you can SELECT FROM, JOIN, and FINE-TUNE from any datasource using any AI-Engine.
JOBS:You can automate tasks in MindsDB, by scheduling any query, at whatever frequency or by using TRIGGERS.
Datasources:Connect any data to MindsDB, ths server then excels at translating SQL queries into requests that can access and combine data from a wide range of sources, including Databases, Applications, Vector stores, and much more.

Why MindsDB?, because SQL is an effective declarative language for data manipulation, it’s also an ideal foundation for constructing data-centric AI.

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Use Cases

Here are some AI/LLM use cases: You can use MindsDB for the following Machine Learning use cases: And for the following multi-media use cases: You can use one of MindsDB’s AutoMLs or you can bring your own, pre-trained model.

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Sample AI Workflow

We can automate any workflow end-to-end using MindsDB’s Jobs command.

There are many tutorials that you can follow to see the full workflows in action. For example: AI workflow featuring integrations with Binance (our data source), TimeGPT (our forecasting model), and Slack (where we’ll publish the outputs from the forecasting model). In this scenario, you can seamlessly retrieve real-time trading data from Binance, utilize it as input for a TimeGPT model to generate forecasts, and receive these forecasts as Slack notifications. Check out the Use Cases section for more examples and tutorials.
To find out about data sources available in MindsDB, follow this link.To find out about AI frameworks available in MindsDB, follow this link.

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FAQ/Tips