The challenge with machine learning today isn't that it doesn't work -- many organizations have successfully applied it to their problems -- but rather many peo

The challenge with machine learning today isn't that it doesn't work -- many organizations have successfully applied it to their problems -- but rather many people struggle to use it. There is massive potential if we can just make it easier to use the machine learning techniques that have already been proven to work. Phds are useful if you're trying to use the state of the art, but that's not what the masses need to benefit from machine learning. The scikit-learn library is a great example of this. It provides a clean fit()/predict() API that developers can leverage in their applications, which little understanding of how the implementations of each algorithms works. Another area ripe to be made easier for new practitioners of machine learning is feature engineering. Without proper feature engineering it is difficult to create accurate models. That is why I work on an open source python library for automated feature engineering called, Featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos https://www.featuretools.com/demos. In the future, I expect even more tools to emerge to help with things like defining a specific prediction problem and extracting labeled training examples, frameworks for robust testing, etc.