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Artem Oppermann | Dec 02, 2022

Automation has become a common buzzword in the ongoing conversation about artificial intelligence, as software shows potential to take over the work of accountants, factory workers, writers and even therapists. Now, AI is even beginning to automate itself in a process known as automated machine learning.

Automated machine learning, or autoML for short, essentially has algorithms take over the process of building a machine learning model. It handles the more mundane, repetitive tasks of machine learning, with the promise of both speeding up the AI development process as well as making the technology more accessible.

What Is Automated Machine Learning (AutoML)?

Automated machine learning, or autoML, applies algorithms to handle the more time-consuming, iterative tasks of building a machine learning model. This could include everything from data preparation to training to the selection of models and algorithms — all of which is done in a completely automated way.

In recent years, there’s been a surge of interest in autoML’s potential to simplify the otherwise complex world of machine learning. DataRobot is often credited as one of the first companies to bring it into public consciousness back in 2013. Since then, Meta has dubbed autoML the “backbone” of its AI, and Salesforce acquired data analytics startup BeyondCore to create its own Einstein AutoML Library. Meanwhile, major tech behemoths like Google, Microsoft and Amazon have rolled out their own low-code machine learning tools that utilize autoML techniques. 

Such widespread industry adoption is significant considering the expertise needed to build cutting-edge AI systems is in such short supply — even at companies like these.

“To me, I don’t see another way forward except for these more automated approaches,” Sarah Aerni, a VP of machine learning and engineering at Salesforce, told Built In. “There are too many opportunities for AI and simply not enough people to onboard to the business, onboard to the tech, deploy it into production, monitor it, and continue iterating on it. To me, autoML is where that enters as a solution to scaling.”

Still, although the concept of automated machine learning has been around for nearly a decade, it remains a work in progress. In fact, earlier this year, Baltimore hosted what has been described as the first international conference on the subject, and it focused on what efforts can be made to improve autoML’s accuracy and streamline its performance. If and when AI-made AI does reach its full potential, it could be applied beyond the borders of tech companies, changing the game in spaces like healthcare, finance and education as well. 

“Practically anybody who uses machine learning will also use automated machine learning,” Lars Kotthoff, an assistant professor and researcher at the University of Wyoming’s computer science department, told Built In. He helped organize the recent autoML conference in Baltimore. “Eventually, this will really be deployed everywhere machine learning and AI is used.”

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The Goal of AutoML

At first blush, automated machine learning may seem a bit redundant. After all, machine learning is already about automating the process of identifying patterns in data to make predictions. The process, which relies on algorithms and statistical models, doesn’t require consistent, or explicit programming. Once a machine learning model is built, it can then be further optimized through trial and error and feedback, meaning the machine can learn by experience and increased exposure to data — much like humans do.

In practice, much of the work required to make a machine learning model is rather laborious, and requires data scientists to make a lot of different decisions. They have to decide how many layers to include in neural networks, what weights to give inputs at each node, which algorithms to use, and more. It’s a big job, and it requires a lot of specialized skill and intuition to do it properly.

What Is the Goal of Automated Machine Learning?

The goal of autoML is to both speed up the AI development process as well as make the technology more accessible.

The more complex the model, the more complex the work. And some experts say automating some of that work will be necessary as AI systems become more complex. So, autoML aims to eliminate the guesswork for humans by taking over the decisions data scientists and researchers currently have to make while designing their machine learning models.

“The machine is taking care of the tedious work that the human was doing before but, ideally, really doesn’t want to do,” Kotthoff said. “It’s an augmentation of what the human is doing. In particular, it’s enabling human to focus more on the interesting parts of this. Things like defining the problem, which a machine cannot do at the moment.”

“It’s an augmentation of what the human is doing.”

Eventually, the goal is to get to the point where a person can ask a question of their data, apply an autoML tool to it, and receive the result they are looking for without needing overly technical skills. And while there are a growing number of companies looking to democratize machine learning through autoML, this technology is largely exclusive to people with AI and data science expertise. It’s a tool, not a specific platform; and it’s a tool with fairly narrow uses, according to Kjell Carlsson, the head of data science strategy and evangelism at Domino Data Lab

Carlsson advises customers on ways they can scale their data science strategy and utilize AI more effectively, and he describes autoML as a sort of “booster” or “accelerator” for data scientists.

“It can make it faster for them to discover the features that they want to use. It can enable them to more rapidly narrow down which algorithms they want to use. And they can be helpful in, early on, identifying some problems with your data,” he told Built In. “It can be very useful for the proof-of-concept phase — to figure out, ‘Is this doable?’.”

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How Does AutoML Work?

Automated machine learning, Carlsson said, is “mostly about” supervised machine learning, meaning it gives users information about the outcome that they’re trying to predict by creating a model that identifies patterns in labeled data. There are many types of machine learning, but with supervised learning, tagged input and output data is constantly fed into human-trained systems, offering predictions with increasing accuracy after each new data set is fed into the system. 

For example, if a company wants to be able to predict whether or not somebody is going to buy its product, they first have to have a data set of past customers, organized by who bought and didn’t buy. Then it has to be able to use that data set to predict what a whole new set of customers will decide to do. Or, if you want a computer to be able to identify a cat in a video, you have to first train it by showing it other videos with cats so it is able to accurately identify one in a video it hasn’t seen before.

Supervised learning is one of the most popular types of machine learning, but it is quite hands-on. Automated machine learning automates the selection of different variables in a given data set that should be used in a model, as well as the algorithms needed to create that model. 

In the case of predicting whether a person will buy or not, autoML would be used to parse through the thousands of data points the company has on that person, and decide what pieces of information should be used in making an accurate prediction. It also automates the selection itself, and decides which model makes the most sense. This could be a logistic regression model, a random forest model, some sort of ensemble model, and so on — whatever is most applicable to the business use case.

Because autoML algorithms operate at a level of abstraction above the underlying machine learning models, relying only on the outputs of those models as guides, they can also be applied to pre-trained models to gain fresh insights without having to repeat existing research or waste computation power. Some of the really good ones will even try out different permutations of different selections to see whether they can give users any uplift in the accuracy of their model, Carlsson said.

Exactly how long autoML takes depends entirely on the amount of data being fed into the model, as well as how many different types of models are being applied. For standard, structured data sets (like customer data in a CRM, for example), Carlsson said it can be “super quick” to run an autoML model — as little as just a few seconds. In larger data sets, where the user wants to try out lots of different model permutations of different algorithms to use, it could take days or even weeks.

An example of this is another category of automated machine learning that enters the realm of computer vision, which uses AI to train computers to analyze and understand on a much more advanced level. For instance, a cashierless checkout system at a restaurant could allow diners to put their plates under a camera that will automatically estimate how much their meal costs. This utilizes a specific class of models called convolutional neural networks, which work exceptionally well on images, and can be “really computationally intensive,” according to Carlsson.

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AutoML Examples: Using AutoML in the Wild

All of this is to say that autoML can be used on advanced, cutting edge artificial intelligence applications, or simple problems often found in conventional businesses that simply don’t have the humans to do it all.

Salesforce is a longtime leader in that area specifically. The company has thousands of customers that are looking to predict a variety of things — from customer churn to email marketing click throughs to equipment failures. And all of this requires lots of rich data that is unique to their specific business, which can be used to build customized machine learning models. Salesforce is focused on making the creation of these models easy and accessible to everyone through automated machine learning — “build it in Salesforce, deploy it in Salesforce,” Aerni said.

AutoML Use Cases

  • Predicting customer churn
  • Building customer lifetime value models
  • Predicting equipment failures
  • Grouping like products together on an e-commerce site
  • Predicting the success of an email marketing campaign

“In order to leverage that data,” she explained, “[Salesforce is] not able to look at it. So we need to use automated machine learning approaches to train on that customer’s data set, in order to transform that data.” This extends into various stages of the machine learning process, from data preparation to training and selecting models and algorithms that are most appropriate — all of which is done in a completely automated way.

“Data science, AI, requires a lot of very skilled, specific expertise,” Aerni said. But autoML is an “out of the box solution” that “democratizes” a process that otherwise has a fairly steep learning curve for average business users. 

Salesforce also focuses on explainability, allowing users to interrogate the models being made. “What’s powerful about that is that it’s really putting power into the hands of the business users so that they can connect to what the data is saying,” she continued. “If you look at the model and the interpretation, or the individual attributes of the data that are correlated with the outcome, it’s very interesting how the business then starts learning about the data.”

“It’s really putting power into the hands of the business users so that they can connect to what the data is saying.”

While there are certainly many paid autoML services with plenty of bells and whistles, there are just as many free, open-source tools, too. And lots of these tools are low code or no code, with the intention of making it easy for non-data scientists to play with this technology as well. Large companies like Airbnb, Home Depot, Chevron and more have all dabbled in autoML to further their business goals. 

“Folks are definitely using autoML tools,” Carlsson said. “They’re more available than ever before.”

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Meanwhile, Arjit Sangupta, the founder BeyondCore, which became Salesforce’s Einstein after it was acquired, created a company called Aible, with the goal of helping anyone build an AI model that creates value. His goal?: “How do we empower everyone to be able to extract value from their data using AI?”

Aible does this by offering a suite of software. One tool focuses on augmented data engineering, another is augmented analytics, providing companies with key insights into their data in language they can understand. And a third offering is augmented data science and machine learning, where it handles the predictive model building while also factoring in all the benefits of correct predictions and cost of incorrect predictions. 

For example, “What’s the benefit of correctly telling you that somebody will buy? What’s the cost of incorrectly telling you somebody will buy when they wouldn’t? And how much capacity do you have to pursue these prospects?” he explained. “And then our system automatically generates an AI or a set of AI that would create the most economic value, given your unique business.” 

 

Challenges of AutoML: Where It Falls Short

Although Aible has been ranked among the top autoML tools out there, Sangupta said he wants to distance the company from what is traditionally thought of as automated machine learning, mainly because he considers traditional autoML to be “completely useless.” 

Challenges of AutoML

  • Can’t understand the business context of the problem it is trying to solve
  • No standard for what a “good” model looks like
  • Doesn’t offer the “why” of its decision-making process
  • Too complex for non-data scientists to pull off successfully
  • Can’t automate ethics or fairness

“The problem with traditional autoML is that it doesn’t start from the business reality,” he added. “It just tries various parameters and a bunch of models, and comes back and tells you ‘Here is the best model.’ And that genuinely is completely useless.” 

“The problem with traditional autoML is that it doesn’t start from the business reality.”

Here’s an example: Imagine the benefit of a sale at your company is $100, and the cost of pursuing a lead is $1. You might be okay with relying on a machine learning model that gives you 99 wrong predictions for every one person that buys $100 worth of product. But, then let’s say your sales capacity only permits 20 calls. That creates a whole new set of restrictions. 

When autoML is used this way, it’s essentially like “using a machine gun to shoot [oneself] in the foot,” Sangupta said. 

“AutoML lets you do things fast. But if you’re doing the wrong things, it’ll help you do the wrong things incredibly efficiently.”

“You have a machine gun, but you haven’t told them where to point it. AutoML lets you do things fast. But if you’re doing the wrong things, it’ll help you do the wrong things incredibly efficiently,” he continued. 

Aible’s answer to that is augmented data engineering, which focuses on getting the data right without human interaction, augmented analytics, and fostering a collaboration with business users so they can understand what is going on with their data and, by extension, the problem they’re trying to solve. 

Then, through its augmented data science and machine learning capabilities, the company is “teaching the AI system to speak human, instead of a human [learning] to speak AI,” as Sangupta put it. “The problem with autoML is that it is so powerful that if you don’t think through what you’re trying to do, and if you can’t explain the impact of that model on the business that you’re trying to understand in language that business users can understand, you can really hurt yourself.”

 

AutoML: A ‘Solution Looking for a Problem’

That is perhaps automated machine learning’s biggest shortcoming: Its lack of business intuition. AutoML will certainly produce a production-ready model more quickly, but it won’t necessarily tell a user why they should use a particular model or what the business justification is — let alone offer a justifiable problem to solve amid a massive set of data.

“It’s been a solution looking for a problem for quite a long time,” Domino Data Lab’s Carlsson said.

Another issue that comes up is that there’s no set standard for what a “good” AI model looks like. Is it based on just accuracy? Does speed contribute? Or its ability to learn? Either way, Carlsson said those metrics very rarely match up to what the business problem actually is. 

“The joke is that all of us can create a model that will predict terrorist activity with 99.99 percent accuracy — we just predict that there’s never any terrorism,” he said. “Terrorism happens so infrequently that if I just predict that terrorism never happens, I’ve got this super accurate model. But it’s a useless model.” Meanwhile, if you created a model that predicted whether or not a player should take another card in a game of blackjack with 50.1 percent accuracy, “that would make me phenomenally rich,” he added. 

In short: It’s all relative, and autoML models can’t tell whether its own predictions are useless or not. And no matter how complex automated machine learning is, it doesn’t offer the “why” of its decision-making process, which is something most of us crave when it comes to trust. 

But University of Wyoming’s Kotthoff said it is “quite challenging” to actually achieve that, especially in the case of autoML, “because of the complexity of this whole machinery and the many decisions that are being made automatically under the hood.”

Actually, this complexity means automated machine learning (somewhat ironically) requires the same kind of expertise this technology seeks to automate in order to get it running. AutoML can’t automatically select a business problem to solve, or specific data to use. It doesn’t automatically integrate with the rest of a company’s problem. And all of this can lead to broken models or biased ML models.

“That’s the danger with autoML is you end up doing the wrong business things and you do the wrong ethical things because the only thing the autoML system understands is the data.”

“In the world of autoML, you can imagine that if you give this to somebody who hasn’t had maybe as much experience on it, you easily can get stuck in it,” Salesforce VP Aerni said.

AutoML doesn’t automate ethics either. There is no built-in conception of fairness. You can impose different constraints in an effort to be fair — like equal rejection rate, equal acceptance rate, equal likelihood of success — and then make sure that the AI serves that definition of fairness, but Sangupta says that falls outside the scope of what autoML is capable of doing because humans have to set those constraints.

“That’s the danger with autoML is you end up doing the wrong business things and you do the wrong ethical things because the only thing the autoML system understands is the data,” he said. 

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The Advantages of AutoML

All that being said, there are certain problems that are really well suited to automated machine learning. These are the problems that require the creation of hundreds of thousands of models, and then updating those hundreds of thousands of models on a regular basis. 

More often, these are classified as forecasting models, Carlsson said. For example, if a healthcare provider wanted to predict demand for different units across their network of hospitals, they would need to not only create different models for each hospital, but also the different units within those hospitals, as well as different time frames (one week out, three months out, and so on). In the end, you end up with thousands of models, the creation and re-training of which requires an immense amount of work for a human data scientist.

“AutoML models work really, really well in these kinds of instances,” Carlsson said.

And autoML generally isn’t prone to the same kind of forgetfulness or shortsightedness that we humans are — especially when faced with big, complex problems.

“Using these automated approaches tends to get better results than humans can achieve, simply because the machine doesn’t make mistakes.” 

“Using these automated approaches tends to get better results than humans can achieve, simply because the machine doesn’t make mistakes. It takes all of this information I gathered in a principled fashion and then makes the decisions based on that, where humans are prone to forget things,” Kotthoff said.

But, of course, the biggest advantage of automated machine learning is that data scientists don’t have to do the hard, monotonous work of building ML models manually anymore, he added. “It’s really something that, in the end, will enable humans to work better and do more work in a small amount of time because they don’t have to do the tedious parts.” 

 

Does AutoML Spell the End of Data Scientists?

Like all aspects of automation, autoML is not immune to the ongoing speculation of it replacing human employees, particularly those working as data scientists. Indeed, the “democratization of data science” was the buzz-phrase when DataRobot first brought this technology to public attention, and it has been reiterated by everyone from Salesforce to Google. But the idea of a business being able to use this technology with absolutely no assistance from data scientists whatsoever hasn’t quite panned out, according to Carlsson.

“Because people don’t know what data scientists do, there is this view of ‘Well, if we have the right tools then everybody will be able to do this and we won’t need data scientists anymore.’ I have really never seen that be true,” he said, adding that, if anything, he’s seen folks move in the opposite direction. Companies are hiring more data scientists. And training more data analysts so they can become data scientists.

In fact, Carlsson says, not only will autoML not replace data scientists, but data scientists are really the only people who benefit from this technology at all. And even then it’s only “incrementally beneficial” to them, mainly because they require so much additional guidance.

“When you’re doing the world of data scientists, the actual creation of the model is just one small part of this,” Carlsson said. Data teams might use autoML a little in the beginning to do some exploratory analysis, but when it comes down to making the “real model,” he added, they’re going to create it from scratch themselves. “It turns out, you actually need folks who understand the data, know how to look at and analyze the distribution of that data, and know how to analyze the results of that data — the validation of the data — in order for you to create a model that actually makes any sense.”

And Aible founder Sangupta says the folks who are worried about autoML replacing data scientists outright are missing the point altogether. He doesn’t think giving everyone the ability to build an AI model that creates value means we have to get rid of data scientists at all. Instead, he likens what Aible does to what the Netscape browser did for widespread internet adoption in the 1990s — it made this foreign and incredibly complex new world more accessible to everyday people. 

“Every technology goes through this phase where, initially, you have these experts and only the experts can do it. But the real potential comes when everyone is empowered to leverage it. That’s what’s going to happen with AI. It has to happen,” Sangupta said. Otherwise, the power disparity between the “AI have and have-nots” will continue to grow.

Indeed, what artificial intelligence is capable of now is vastly different from what it was even just a few years ago, and it has had huge implications on how businesses are run. Ordinary chatbots are beating the Turing Test, AI is keeping pace with increasingly sophisticated cybercrime, and sales teams are working with more precision and information than ever.

“Our world is changing so fast that, without AI, you can’t compete,” Sangupta said. “When the internet revolution came about, a lot of companies that didn’t get on board died out. I actually think the AI revolution is going to be far more disruptive than the internet revolution ever was.

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