These days, everyone’s getting in on Machine Learning (ML). It’s definitely a great direction to pursue for many businesses since it gives them the ability to deliver tremendous value in a fairly quick and easy way. The demand for machine learning skills is at an all time high. There’s a nice comprehensive report done by McKinsey about how AI is shaping industries and where the opportunities are.
The supply greatly outweighs the demand in AI and Machine Learning
And so we see every business around say:
Hey, we need a machine learning research team really quick! We’ll get the best scientists with lots of publications and pay them lots of money so we can get some machine learning in our business. Hooraaayyy!
But wait just a minute… As a business, do you really need a machine learning research team? Will your business even use them effectively for today’s high price? How much do you even need Machine Learning at all? Is it really that complicated?
If you’re more of a technical person, do you go full throttle on learning how to do machine learning research?
To answer this question we need to differentiate between the two types of ways we can really work with machine learning: research and application.
Machine learning research
Machine learning research is really all about the science. A machine learning researcher is trying to push the boundaries of science, specifically in the field of Artificial Intelligence. These people typically have a Masters or PhD in CS and have many publications in top machine learning conferences. They’re super popular in the research space!
The machine learning researcher is fantastic if you’re doing something really cutting edge. These people are used to finding custom scientific solutions to your problems. If you were to tell them “We’re really good at automatically detecting human intruders using face recognition with 95% accuracy. Could you get us up to 97%?”. The ML researcher is your go to guy!
Machine Learning researchers know this stuff
Here’s the catch: this person likely hasn’t ever actually deployed software into production! They probably aren’t experts at delivering Software as a Service (SaaS) or as a product to your customers, translating the research into practice. They won’t know how to properly package, productionalize, and ship.
That’s where this next one comes in …
Machine Learning application
Machine learning application is all about the engineering. A machine learning engineers knows how to take the latest ML research and translate it into something valuable. They take the research and put it into a product or service. These people are very good with cloud computing services such as AWS from Amazon or GCP from Google.
Machine Learning engineers know this stuff
Unfortunately people with these skills are often overlooked by businesses looking to integrate Machine Learning into their products or services. The Machine Learning engineer is often hidden in plain sight, having extensive experience in deploying cutting edge products and plus enough knowledge of machine learning to use it.
The Machine Learning engineer isn’t as fancy as the researcher since they don’t look like ML superstars with a PhD and 5000 citations. But you need them if you ever want to deliver your ML driven product to customers.
How to use Machine Learning in your business
Deciding on how to use Machine Learning in your business and build your team will depend on the product or service you are trying to deliver. Is the thing you’re building super custom, going beyond the current state-of-the-art in AI or in a totally different direction? You’ll probably need some ML researchers to get the jobs done, they’re used to doing that type of thing.
For most businesses and teams, you really don’t need that. Much of the current Machine Learning science is good enough for many applications. It’s not that complicated. You don’t need someone to reinvent the wheel, you need someone who knows how to use the wheel to make your car better: an engineer!
At the end of the day, machine learn is a tool, just like any other software tool. Researchers create the new tools, engineers figure out how to best use them. Machine Learning does some really cool things now!… But it’s purpose is still primarily to eventually deliver some kind of value to consumers.
Let’s keep that in mind through the hype!
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