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.

AutoML vs. Data Scientists

A little respect is due—in both directions.

Can Automated Machine Learning (AutoML) beat serious data scientists in producing accurate predictions? People who understand data science and programming deeply, when armed with all the tools and computer resources and time they need, are nearly always able to produce better models than generalized, self-programming AutoML systems. There are two questions to ask before you try solving problems with specialists:

How much time and money do you have?
Hand-crafting machine learning models means projects will take from days to months to complete. Expert data scientists are costly. The investment in their work must exceed the value of their predictions by the expected rate of return to make projects viable. Custom work is best suited to very-high-value outcomes where generalized tools do not produce the required accuracy, and where there is time to build and test the solutions before the questions change.

How good must your models be?
Not all predictions have life-and-death consequences. Marketing decisions, for instance, can benefit from improving accuracy from “coin-flip” to 70%. Waiting to achieve 73% with fewer false positives/negatives rarely pays compared to acting on good-enough data. When detecting serious diseases, that same 3% in accuracy with lowest false results could determine who survives. Surgical precision is essential for surgery, but you wouldn’t bake an apple pie that way.

The takeaway: We love data scientists and employ them. Their knowledge of mathematics, statistics, algorithms, and code is irreplaceable—for the appropriate tasks. Use their rare talents for your most critical work. For most ordinary business decisions, AutoML results are remarkably accurate and available in minutes at very low cost.

How “Auto” Is That AutoML?

Some AutoML systems are more automatic than others.

AutoML stands for Automated Machine Learning, meaning streamlining the end-to-end process of solving problems with machine learning. Steps that need to be automated include:

  • Data Preparation (type and dependency)
  • Feature Engineering
  • Model Algorithm Selection
  • Training
  • Hyperparameter Tuning
  • Model Evaluation
  • Production Processing and Deployment

In practice, there is a spectrum of “auto-ness” starting at “barely better than coding from scratch.” Here are some clues that a particular instance of AutoML may not be so automatic:

  • Mentions Jupyter notebooks
  • Requires knowledge of Python
  • Refers you to GitHub
  • Asks for hyperparameter ranges
  • Makes you select an algorithm
  • Omits performance metrics in output

The takeaway: Don’t assume that AutoML in the product’s description means that you can use it without programming or manual inputs.

How to Prove AI Value to Skeptics

Make a prediction that comes true.

AI has not taken its seat at the decision making table for many organizations that would reap big benefits from it. That’s easy to understand, since the terminology—Artificial Intelligence, machine learning, robotic assistants, and the like—are conflated in stories and ads to the point of being meaningless. Nearly every system claims to be AI-driven, without explaining what that means.

So what do you do when you are an executive who understands the concept and the promise of AI, but can’t get past the barriers? Building a data science team is an expensive leap of faith before you quantify potential gains. Automated machine learning is the best way to show AI’s value swiftly—by making accurate, actionable predictions that deliver better results than your current processes.

Pick a practical problem. Think of areas where classification or regression predictions could add revenue or save cost.

  • Will this happen or not?
  • Which of several things will happen?
  • How much will happen?

Making predictions with AutoML is as simple as pointing to training data, selecting which values you want to predict, and setting it to work on your data. What returns is record-by-record likelihood of future outcomes. Those are easy to monetize through programs that amplify high-probability actions and avoid low-probability ones. The results can be startling. One of Squark’s customers applied AutoML to a marketing cross-sell problem and had this to say:

“Double-digit uplift in sales overnight, with no coding. Remarkable.”

The takeaway: AutoML proves the value of AI quickly. Pick a prediction use case and demonstrate how a view into the future transforms decision making with instant ROI.