What is Automation and Machine Learning?

Automation and machine learning have been responsible for a great deal of innovation and development over the years. It’s ultimately culminated in something called automated machine learning, or AutoML. It represents a fundamental shift in how organizations of any size today approach both machine learning and data science. Below, you’ll learn what it’s all about and why it’s so important for the future.

Automated Machine Learning

Applying your traditional machine learning methods to real world problems now demands a lot of your time, resources, and has become too challenging for the results that you get. It also requires you to be an expert in several disciplines. This is on top of data scientists being needed – a profession that is sought out a lot these days.

The development of automated machine learning changes all of that as it makes it easier for someone to build using machine learning models in the real world. It’s easier because it runs a systematic process on raw data and just selects models that pull the most relevant information from that data.

Here is what a standard machine learning process looks like:

Figure 1

When you’re creating a model using traditional methods, the only thing you’ll notice that’s automatic in the whole thing is the training. What automated machine learning software will do is perform the tasks that would require skilled data scientists to do. Things like the manual and tedious modelling tasks.

The reason for that is the traditional process would require a data scientist weeks, or even months, to be doing all of this. AutoML would do all of this in a matter of days and come up with several dozens of models.

Here is what an AutoML model would look like:

Figure 2

By automating a lot of the steps, you’re given results at a greater speed. It also democratizes data science, allowing people with no data science background to understand everything.

Why Is This Important?

First, to manually construct a machine learning model takes multiple steps and it demands a lot from the creator. They need to have domain knowledge, mathematical expertise — and of course knowledge in computer science and have the skills to do it.

In short, it’s a huge ask for a single company, let alone a single individual or team.

And let’s keep in mind that this isn’t even considering the countless opportunities for errors and our biases which can degrade the accuracy and insights of models too.

With AutoML, you use the knowledge that is baked-in to data scientists without needing to spend a lot of time or money. You also improve your return on investment since this reduces the amount of time it takes for you get value.

AutoML works fantastic in many different industries already with some prime examples being:

  • Health care
  • Financial markets
  • Banking
  • Fintech
  • Public sector
  • Marketing
  • Retail
  • Sports
  • And manufacturing

All in all, AutoML removes a lot of the “busywork” of data scientists, but doesn’t replace them. This is simply a tool that allows data scientists to put their time to focus on more complex problems that can help companies grow.

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