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8 posts
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It’s Complicated: On Imagination, Marvin Minsky & Shawn Carter

Computers remain dependent on humans to be able to imagine & architect what the computer should & can do. And perhaps we’re not building machines to be able to do anything beyond specialized intelligence because we don’t yet have reverence for the power of imagination and all that is required to discover and execute better ideas.
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Characteristics of Machine Learning solutions

It is estimated that 87% of data science projects never reach production. One of the pitfalls to developing a production-ready machine learning solution is the ability to define if it's an appropriate tool for solving the problem. Not every problem should be solved with machine learning.
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Data Iceberg Model for Machine Learning

One of the pitfalls to developing production-ready machine learning solutions is the failure to identify the appropriate data assets. In evaluating the data assets to be used for your project, use the Data Iceberg Model approach to determine the underlying (i.e. not visible) structures that triggered the creation of the dataset. The following Data Iceberg Model can be used to evaluate the quality & limitations of the data used to train your machine learning models.