From Data Science Studio to Production : A Model’s Journey
The Measurement Minute with Gary Angel If building models is a snap (as per the last Measurement Minute), operationalizing them definitely isn't. In our work, moving models for ML Studio...
Machine Learning: Model building isn’t the hard part
The Measurement Minute with Gary Angel Machine Learning is at the stage just before widespread adoption when lots of potential users are worried about how hard it is. It isn't easy (not ...
Variation Bottlenecks – How Creative can Limit Analytics
The Measurement Minute with Gary Angel Analytics - and especially machine learning - require lots of variation. Often enough that's not a problem. But particularly when it comes to creat...
Actionable Analytics
The Measurement Minute with Gary Angel Everyone (including me) likes to tout analytics ROI and actionable analytics. But the truth is that direct, actionable results from analytics are l...
Loss Prevention and Shopper Analytics
The Measurement Minute with Gary Angel Combining loss prevention and shopper analytics sure looks attractive when you consider the cost and friction in duplicating your in-store camera i...
Garry Kasparov, Ke Jie….Bill Belichick?
The Measurement Minute with Gary Angel If you consider the factors that control what makes a good ML problem and apply them to major sports leagues, it's pretty clear what role stands ou...
Scale Efficiency and ML – Why Our Short-Attention Span Matters
The Measurement Minute with Gary Angel Thinking about when to use machine learning? Defined outcomes and clock-time efficiency are both important. But so too is the scale of the problem ...
McDonald’s Just Bought Dynamic Yield for $300 Million – It’s not your typical acquisition
The Measurement Minute with Gary Angel Acquisitions are a dime (or $300 million) a dozen these days. But McDonald's acquisition of Dynamic Yield is definitely an out-of-the-box strategic...
The Surprising Role for Humans in Creating ML Training Data
The Measurement Minute with Gary Angel Creating training data for ML can be really hard. That's why you should consider using the data you have to generate more. Check out the full blog ...