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FAQ

Why MindsDB?

We are building MindsDB, because we want to Democratize Machine Learning. If you want to learn more about why this is important and how we aim to do this, you can check out our presentation here.

Who are we building MindsDB for?

We are building MindsDB for all of those that can get their hands in data. and can type a few lines of code.

What are MindsDB's current Goals?

MindsDB has 3 simple goals.

  1. Provide a way to learn and predict from data with a line of code.

  2. Explainable, by answering the following questions:

  3. When learning:
    • What is interesting in my data and why?
    • When can I trust this model and why?
    • When I should not trust this model and why?
    • How can I improve this model?
  4. When predicting:

    • Why this prediction?
    • Why not something else?
  5. Remain state of the art. Since MindsDB users are delegating the ML machinery, MindsDB should try to always generate state of the art models for the users.

What is the roadmap?

MindsDB roadmap is aimed to be aligned with our goals:

Success

  • versions 1.0
    • MindsDB GUI where you can visualize explainability goals
    • Support for images and complex text
    • MindsDB REST API's

Success

  • versions 2.0
    • Run Machine Learning Models as Tables

Important

  • versions 3.0
    • Auto ETL and Data Preparation

What type of data can MindsDB learn and predict from?

We support tabular data, this is CSV, Excel, JSON, text files, pandas data frame, URLs, s3 files or MySQL, Mongo, ClickHouse, PostgreSQL data stores. For more information please see the data sources documentation.

How does it work?

In very simple terms, MindsDB follows the following steps:

  • to learn:
    • break data source intro train, test, validate
    • transform data source into tensors
    • build and train encoders (if necessary)
    • produce a neural network based model that can take in the input tensor and produce the target tensor
    • break train data into batches and try learning a model that can fit the target
    • test and validate until model convergence
    • store metadata about the most fit model
  • to predict:
    • transform question data into input tensor
    • load most fit model
    • run input tensor into model
    • transform output tensor into readable output

You can learn more about the internals of mindsdb here.

How can I help?

You can help in the following ways:

Why is it called MindsDB?

Well, as most names, we needed one, we like science fiction and the culture series, where there are these AI super smart entities called Minds.

How about the DB part?. Although in the future we will support all kinds of data, currently our objective is to add intelligence to existing data stores/databases, hence the term DB. As to becoming a Mind to your DB.

Why the bear? Who doesn't like bears! Anyway, a bear for UC Berkeley where this all was initially coded.

What is the difference between AI and Machine Learning?

What is XAI?