Debunking automated ML myths
Much like the moon landing, when it comes to automated ML, there are more than a few conspiracy theories and myths surrounding it. Let's take a look at a few that have been debunked.
Myth #1 – The end of data scientists
One of the most frequently asked questions around automated ML is, "Will automated ML be a job killer for data scientists?"
The short answer is, not anytime soon – and the long answer, as always, is more nuanced and boring.
The data science life cycle, as we discussed previously, has several moving parts where domain expertise and subject matter insights are critical. The data scientists collaborate with businesses to build a hypothesis, analyze the results, and decide on any actionable insights that may create business impact. The act of automating mundane and repeatable tasks in data science, does not take away from the cognitively challenging task of discovering insights. If anything, instead of spending hours sifting through data and cleaning up features, it frees up data scientists to learn more about the underlying business. A large variety of real-world data science applications need dedicated human supervision, as well as the steady gaze of domain experts to ensure the fine-grained actions that come out of these insights reflect the desired outcome.
One of the proposed approaches, A Human-in-the-Loop (HITL) Perspective on AutoML: Milestones and the Road Ahead by Doris Jung-Lin Lee et al., builds upon the notion of keeping humans in the loop. HITL suggests three different level of automation in data science workflows: user-driven, cruise control, and autopilot. As you progress through the maturity curve and the confidence of specific models increases, the user-driven flows move to cruise control and eventually to the autopilot stage. By leveraging different areas of expertise by building a talent pool, automated ML can help in multiple stages of the data science life cycle by engaging humans.
Myth #2 – Automated ML can only solve toy problems
This is a frequent argument from the skeptics of automated ML – that it can only be used to solve well-defined, controlled toy problems in data science and does not bode well for any real-world scenario.
The reality is quite the contrary – but I think the confusion arises from an incorrect assumption that we can just take a dataset, throw it to an automated ML model, and we will get meaningful insights. If we were to believe the hype around automated ML, then it should be able to look at messy data, perform a magical cleanup, figure out all the important features (including target variables), find the right model, tune its hyperparameters, and voila – it's built a magical pipeline!
Even though it does sound absurd when spoken out loud, this is exactly what you see in carefully crafted automated ML product demos. Then, there's the hype cycle, which has the opposite effect of diminishing the real value of automated ML offerings. The technical approaches powering automated ML are robust, and the academic rigor that's put into bringing these theories and techniques to life is like any other area of AI and ML.
In future chapters, we will look at several examples of hyperscalar platforms that benefit from automated ML, including – but not limited to – Google Cloud Platform, AWS, and Azure. These testimonials lead us to believe that real-world automated ML is not limited to eking out better accuracy in Kaggle championships, but rather poised to disrupt the industry in a big way.