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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: * 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? * When predicting: * Why this prediction? * Why not something else? 3) 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:

  • versions 1.0 and greater will focus on for goal 2 (Early April 2019)

    • What to expect:
      • MindsDB UI where one can visualize explainability goals.
      • Support for images, audio both as input and output and complex text (e.g. translation) as output.
  • versions 2.0 and greater will focus on goal 3 (?).

    • What to expect:
      • Simpler modularization of meta-model building for ML/Experts to contribute.

What type of data can MindsDB learn and predict from?

Currently, we support tabular data, this is CSV, excel, json files and urls or relational 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.