You Ask, I Answer: Will Automated Machine Learning Make Data Scientists Obsolete?

Sheedeh asks, “Will new advances like automated machine learning make data scientists obsolete?”

Most definitely not, though I can understand why that’s a concern. AI is currently automating a fair number of tasks that data scientists do, but those tasks are relatively low value. I’ve had a chance to test out a bunch of automated machine learning frameworks like IBM’s AutoAI and H2O’s AutoML. The new features are time savers for data scientists, but cannot do what data scientists do. One of the key areas where automated machine learning is, and for the foreseeable future, will fall short is around feature engineering. Watch the video for full details.

Recall that there are 5 key types of feature engineering:

  • Feature extraction – machines can easily do stuff like one-hot encoding or transforming existing variables
  • Feature estimation and selection – machines very easily do variable/predictor importance
  • Feature correction – fixing anomalies and errors which machines can partly do, but may not recognize all the errors (especially bias!)
  • Feature creation – the addition of net new data to the dataset – is still largely a creative task
  • Feature imputation – is knowing what’s missing from a dataset and is far, far away from automation

The last two are nearly impossible for automated machine learning to accomplish. They require vast domain knowledge to accomplish. Will automated machine learning be able to do it? Maybe. But not in a timeline that’s easily foreseen.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode she asks Will new advances like automated machine learning, make data scientists obsolete? Most definitely not. Though, I can understand why that would be a concern because obviously, automated machine learning makes many promises like it’ll speed up your your AI pipeline, it will make your company faster, data scientists are expensive, and so on, so forth. But a lot of the promises that they’re being marketed about AI, automated AI are falling really short. AI is and should be automating a fair number of tasks that data scientists do. But those tasks are the low value. And one hot encoding a table is a low value task from the perspective of if you’re paying 300, 400 $500,000 a year for this person, having them encode a table is something that a machine should definitely do, it’s not the best use of the time. And a lot of these newer automated frameworks, make the promise that they’ll handle everything for you, you just put in the data and magic happens. I’ve had a chance to test out a bunch of these frameworks. These automated machine learning frameworks, IBM is auto AI, h2o is auto ml remixes auto ml. And the features that are in these toolkits are time savers, for sure, for data scientists, but they can’t replace a data scientist. They can augment they can reduce some of the repetitive tasks, the low value stuff, but they’re not a replacement for the person. I’ll give you an example one of the key areas where automated machine learning really falls short. And will for the foreseeable future is around feature engineering. feature engineering is a fancy term in data science for essentially, college in a table, right, so if you have a spreadsheet, it’s the columns in your spreadsheet. And there’s five key types of feature engineering, some machines can do, well, some can’t. As an example, let’s let’s imagine a table with four features, right? The date that you brewed a cup of coffee, the temperature of the coffee, what being type used, you know, Colombian or Nicaraguan whatever, and an outcome was a good cup of coffee or not. And you want to know what makes for a good cup of coffee, we’ve got a table with four features, it’s not a whole lot of data to build a model on feature engineering is all about creating and updating and tuning your data so that you can build a better model. And that model can then be used to predict whether the next cup of coffee you’re about to brew is going to be good or not. Right. So we have date, temperature being variety, and outcome was it a good couple. So the five areas of feature engineering, number one is extraction. This is where machines really shine easy to do. If you have the date that you brewed a cup of coffee, one of the things in there, you have the day of the of the week, you have the day, you have the day of the month, the day of the year, the day of the quarter, you have the week of the year, you have the quarter, you have the month, you have the hour, the minute, the second, and so on, so forth. So you can expand that one field into a bunch of new fields. This is called feature extraction. And it is something that machines can do super well. So you could take that date and explode it, maybe there’s maybe the hour of the day that you were a cup of coffee matters, we don’t know. But you could you could expand that.

The second type of feature engineering is called feature estimation. And this is where you it’s called predictor importance or variable importance. Let’s say that you expand that date field, all those possible variations. And then you run a machine learning model. With the desired outcome being it was a good cup of coffee does day of the week matter. When you run the model, the machine can spit back estimations of important that say no day doesn’t matter. But our the day does, so can help you tune that. So feature estimation helps you tune your table to avoid adding crap to it all jumbled. All sorts of silly stuff, again, something that machines can do very, very easily. feature correction is the third area. And that is where you’re trying to fix anomalies and errors. machines can partly do that, right? So if there’s a missing date, like you forgot to record a cup of coffee One day, a machine can identify that, again, that’s missing. But they’re getting they’re getting better at but they’re still not great at detecting things like bias, right. So for example, being variety is one of the beans that is one of the features we’re talking about in this this fictional table. If you only buy Columbian coffee, guess what, you got a bias in your data, the machine may not necessarily see that as an anomaly, or as a bias. Like, hey, you only bought one kind of coffee here this whole time. So the the the the feature estimating mattress a this feature doesn’t matter. Well, if you know anything about coffee, bean varietals matters a whole lot. But if you’ve only tested one kind, you got a bias in your data and the machine won’t know to detect that, in fact, they’ll come up with the wrong answer and tell you to delete that column. The fourth area is feature creation.

This is

a creative task, being able to to create net new features on a table. So say we have been a variety in there, a machine can look at the data set. And if you got Colombian and a Nicaraguan and all this stuff, it can categorize that, but it can’t add net new data, like an easy thing for us to do would be to add the price that we paid for that can of beans. machine doesn’t know to ask for that he doesn’t even know how to get that doesn’t know that it exists, we, as the humans would need to create that feature, we need to bring in additional outside data was not in the data set in order to create it. So feature creation very difficult for machines, do you need domain expertise to do that, and a follow on Fifth aspect of feature engineering is feature amputation, which is, you know, as the expert, what’s missing from the data set, right. So for example, you brewed that cup of coffee, you got the temperature of the cup of coffee, great. I know as someone who drinks coffee, that there is depending on the carpet served in depending on the time of day, the ambient temperature, there is a lag time between the time was brewed, and the time you put it to your mouth and start drinking it. How long was that time, it’s not the data set. And it’s and you as a data scientist need to know, hey, if somebody let this cup of coffee, sit on the counter for 10 minutes, it’s gonna be a very different temperature that comes right off of the machine. But that is again, knowing what’s missing from the data set cooling time is missing from the data set completely. And so as a domain expert in coffee, you would know this needs to be in there. And so automated machine learning can make the most of the data that you provided. But it can’t really do a great job of detecting bias. It can’t do bring in new data for feature creation and it can’t really can’t do feature amputation. So a lot of these automated machine learning frameworks if you hear companies saying this will this will you know, this is magic all you need to do is putting your data and the rest leave it to the machines? Nope. It will speed up your data process science it will speed up your machine learning it will speed up the outcomes that you get it will make life easier for your data scientists but it is not a replacement. And this is a good example in general of what AI can and cannot do. So will Am I concerned the automated machine learning is going to make data scientists obsolete No. cup of coffee is is a very simple example of just how how off the rails that can go. So good question. important question is the important question to ask. And the answer is you still need data scientist for the foreseeable future. As always, please leave your comments below and please subscribe to the YouTube channel and the newsletter I’ll talk to you soon. want help solving your company’s data analytics and digital marketing problems. Visit trust insights.ai today and let us know how we can help you


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