How to Pick AutoML Use Cases

Hint: Focus on key performance indicators.

Automated Machine Learning (AutoML) can help you make better and more timely decisions by detecting signals in data that would be impossible to see with conventional analysis. To make the most of this power to see the future, attack your most important performance indicators. Remember that AutoML delivers specific, record-by-record predictions—not just generalized insights. That means you can take actions that change outcomes. Apply supervised AutoML when you want these answers:

• Will this happen or not?
  Predictions based on two possible outcomes are called binary classifications.  For example, will the sales opportunity be won or lost; will the customer stay or leave for a competitor; will the package arrive on time or not?
• Which of several things will happen?
  Predicting outcomes when there are three or more possibilities is termed multinomial classification. Which promotional offer will increase sales most; which advertisements on what media work best; on what days and times are problems most likely to occur?
• How much will happen?
  Predicting and outcome where the possibility could be any real number is called regression. What is the revenue forecast; what will this customer’s lifetime value be; what is the maximum temperature the product will reach during transit?

As a bonus, Squark AutoML explains why by highlighting variables that are most predictive. This means you learn which knobs to turn to improve performance and which to leave alone—the essence of turning predictions into profits.

The takeaway: AutoML produces answers quickly, before the questions change. Pick your application and go for it.