There is no alternative of TRUTH

What about self-healing algorithms or self-maintaining AI platforms” you may ask. Well, none are close to production-grade enterprise implementations today. And, it will be a while before they obviate any human intervention.

Business leaders often find it disappointing to learn that, a machine learning model, after consuming precious time and dollars of investment still needs humans for routine maintenance. Let’s see why this is the reality today and how companies can plan for it.

A Handyman’s relevance in an AI World

To understand why models still need humans, we’ll first start with a simple English explanation of how a machine learning model works, on the inside.

We’ll then cover 4 key scenarios where models usually scream for human help.

Finally, we’ll conclude by listing specific steps that businesses must plan for, to fail-proof their implementations and keep their models sane and successful.

A sneak — peek into how typical ML models work

Let us say you are a Telecom company looking to solve the big problem of customer churn. You’d like early predictive warnings on those customers who would migrate out in the coming month.

You duly collect a dozen data feeds about customer demographics, purchases, subscription plans, and service interactions. You hand over these 100+ attributes from the past few years, which run into millions of data points.

Data scientists spend their days and nights analyzing and making sense of all this data. They then build and engineer models that can predict, say 8 of 10 customers who would eventually leave. When piloted, this works beautifully and lets you focus on the task of retaining these customers. All is well, so far.

Let’s pause now, and unwrap this magical model to inspect its internals.

From the 100+ factors of customer attributes that you supplied, you’d find just the key 3 to 5 used by the model. For example, these could be, ‘contract tenure’, ‘whether movies were streamed’, ‘kind of service complaints reported’, or more non-intuitive factors like ‘payment mode’, or the ‘number of dependents’.

After studying the strength of all signals, algorithms usually end up using a tiny set of factors (or maybe just 1!) that are most related to customer churn.

The true essence of all classification models can be encapsulated as: “Just give me a, b, c and I’ll tell you the chances for event z to occur.”

That’s all a model needs, and it does just that, at the unit level.

No, it doesn’t always crunch a million data points. No, it doesn’t model the customer brain nor does it perform any eerie reading of the human psyche. And deep learning models are not an exception either.

The entire discipline of machine learning is about identifying those few factors (predictors) and then figuring out their relationship (imagine a formula) to the outcome (target).