Open Source Automated Machine Learning With MindsDB

Summary

Machine learning is growing in popularity and capability, but for a majority of people it is still a black box that we don’t fully understand. The team at MindsDB is working to change this state of affairs by creating an open source tool that is easy to use without a background in data science. By simplifying the training and use of neural networks, and making their logic explainable, they hope to bring AI capabilities to more people and organizations. In this interview George Hosu and Jorge Torres explain how MindsDB is built, how to use it for your own purposes, and how they view the current landscape of AI technologies. This is a great episode for anyone who is interested in experimenting with machine learning and artificial intelligence. Give it a listen and then try MindsDB for yourself.

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• Your host as usual is Tobias Macey and today I’m interviewing George Hosu and Jorge Torres about MindsDB, a framework for streamlining the use of neural networks

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by explaining what MindsDB is and the problem that it is trying to solve?
  • What was the motivation for creating the project?
• Who is the target audience for MindsDB?
• Before we go deep into MindsDB can you explain what a neural network is for anyone who isn’t familiar with the term?
• For someone who is using MindsDB can you talk through their workflow?
  • What are the types of data that are supported for building predictions using MindsDB?
  • How much cleaning and preparation of the data is necessary before using it to generate a model?
  • What are the lower and upper bounds for volume and variety of data that can be used to build an effective model in MindsDB?
• One of the interesting and useful features of MindsDB is the built in support for explaining the decisions reached by a model. How do you approach that challenge and what are the most difficult aspects?
• Once a model is generated, what is the output format and can it be used separately from MindsDB for embedding the prediction capabilities into other scripts or services?
• How is MindsDB implemented and how has the design changed since you first began working on it?
  • What are some of the assumptions that you made going into this project which have had to be modified or updated as it gained users and features?
• What are the limitations of MindsDB and what are the cases where it is necessary to pass a task on to a data scientist?
• In your experience, what are the common barriers for individuals and organizations adopting machine learning as a tool for addressing their needs?
• What have been the most challenging, complex, or unexpected aspects of designing and building MindsDB?
• What do you have planned for the future of MindsDB?

Keep In Touch

• George
  • Blog
  • George3d6 on GitHub
  • @Cerebralab2 on Twitter
  • LinkedIn
• Jorge
  • LinkedIn
• MindsDB
  • Website
  • @mindsdb on Twitter
  • mindsdb on GitHub

Picks

• Tobias
  • Bose QuietComfort 25 noise cancelling headphones
• George
  • Open CourseWare – Brain And Cognitive Sciences
  • Cerebralab Blog
• Jorge
  • Lightwood
  • MKDocs with Google Material Templates

Links

• MindsDB
  • GitHub
• 3Blue1Brown – Neural Networks
• Think Bayes
• Backpropagation
• Reverse Automatic Differentiation
• Ludwig deep learning toolbox
• Lightwood
• Tensorflow
• PyTorch
  • Podcast Interview
• Aerospike
• scikit-learn

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA