Summary:  Analytic Platforms are rapidly being augmented with features previously reserved for data scientists.  They are presented as easy to use but require substantial data literacy and advanced DS skills for the most complex.  Business users and analysts can pursue more complex problems on their own, but need good oversight.

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Summary:  If you’re planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources.  Read the research, then consult with your own data scientists for a better evaluation of risk. It’s likely not as bad as you think. 

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Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications.  Here are five strategies for getting around the data problem including the latest in One Shot Learning.

For at least the last two years we’ve been in an…

Summary:  We are entering a new phase in the practice of data science, the ‘Code-Free’ era.  Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear.  Here’s what you need to know.

We are entering a new phase in the practice of data science, the ‘Code-Free’…