How to approach machine learning as a non-technical person


Aria Haghighi


Aria is the CTO and Chief Architect at Pioneer Square Labs.

The last few years have seen an explosion of interest in machine learning technology and potential applications. As a non-expert, you’ve probably either had to assess ML technology for your product and business or as a potential investment. The jargon around ML technology is vast, confusing and, unfortunately, increasingly being hijacked by overeager sales teams.

This post is not a primer on ML technology; this post won’t pretend to give you an explanation of deep learning or any specific technology, because these concepts change frequently and are largely irrelevant to much of the decision making. Instead, this post will address how to assess the technology and determine if it will yield pragmatic business value.

Understand the task

Ultimately, ML is meant to be used in the context of a given task, a problem with inputs and a way to objectively assess how right or wrong an output is. While you may not understand the technology being used, it’s crucial to understand the task.

Don’t accept vagueness or something poorly defined like “understanding what a sentence means.” If someone can’t explain what their ML actually does independently of technical jargon, it’s a bad sign.

At a high level there are common kinds of tasks frequently seen in ML: classification, regression and ranking. For instance, image recognition, such as in ImageNet, is a classification task where we have an input image and want to predict the primary subject matter of the image (a photo of a dog, car, etc.).

Regression is about predicting a real numerical value or values from an input, such as predicting the future value of a home or a stock portfolio. Ranking is about predicting an ordering of items which is “best” in a given setting; for instance, in search ranking, we want to order results that are most relevant for a given query and user profile and history.

So when you’re hearing about an ML pitch of some kind, it’s important to take a step back and get an explanation.

Understand the evaluation metric

Once you understand the task, it’s important to understand how the ML system is being evaluated on that task. Typically, people will define a system evaluation metric that gives a quantitative measure of how well the system does on the task. As an example, in image recognition you can report what percent of the time you predict the right category for an image (e.g. I correctly guessed this was an image of a dog). The common ML tasks (classification, regression and ranking) all have standard evaluation metrics with which it would be worth familiarizing yourself.

It’s unfortunately quite common for people to develop very complex algorithms and technology for problems, but not actually develop an objective evaluation metric. Not having a metric is a very bad sign. There’s no objective way to actually know whether their “super deep learning” actually yields any tangible benefits. When it comes to building ML, or any technology really, for business value, you want to work with people who focus and drive by metrics.

A common and frustrating reality is that more complex ML technology does not necessarily mean improvements on evaluation metrics; especially in environments with limited data, simple techniques frequently outperform more complex ones.

The corollary of this is if you’re building ML, always develop and try simpler methods first. I’ve personally consulted on many projects where people have heavily invested in ML only to find out something vastly simpler (in more than one case just Naive Bayes) performed at least as well, with an order of magnitude more speed and less development time.

Understand how ML improvements impact business metrics

The last and trickiest aspect of assessing ML technology is understanding how improvements on the ML task will impact which business metrics and by how much. Sometimes there’s a very direct relationship. For instance, for ad placement in search results, the ML metric is typically predicting the probability of ad click-through (possibly weighted by expected CPC).

The rate and revenue-generated ad click-through is either a core business metric or closely related to one. In this setting, it makes a lot of sense to invest heavily in ML, because gains will likely improve business metrics.

In other settings, the relationship is less clear. For instance, at Netflix, improving movie recommendation quality by 0.5 percent, while difficult, does not necessarily mean that month-over-month subscriber retention will necessary budge (although something like engagement might).

As a product owner or investor, it’s important that you understand which business metric you want to actually move and whether or not ML improvements might actually yield those changes.

Unsurprisingly, this might be part of why Google invests so heavily in ML, because improvements are strongly correlated with key business and financial metrics. On the flip side, for Apple, a 1 percent improvement to Siri has a much weaker and tenuous relationship with how many iPhones are sold.

If you want to work on ML in products or invest in the area, it’s crucial to understand whether this really is an area where ML can “move” the needle.

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