What is Automated Machine Learning

Automated machine learning pairs sophisticated data collection with human-created algorithms that segment historical data so analysts can use it to predict future outcomes. It’s used in every industry — manufacturing, finance, health care, information technology, commerce and more — to solve problems and make decisions.

In this article, you’ll learn about the importance of automated machine learning, data scientists’ roles in the process and common careers that use machine learning.

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Why Is Automated Machine Learning Important?

In science fiction movies and TV shows, the future is overrun by machines and robots that become so smart, they take over the world and threaten to extinguish humanity.

In reality, automated machine learning is created by data scientists so that non-scientists can gather information and make informed decisions.

Why you need automated machine learning

The “automated” part of automated machine learning, which is sometimes shortened to AutoML, is important because it allows data architects to collect data and build algorithms based on historical facts. It can be found in every sector of our economy:

Banking, finance and insurance
Health care
Manufacturing
Marketing
Retail
Sports and entertainment

AutoML enables data analysts to make data-based business and operations decisions. The automation of the process is designed to remove human error, but it isn’t intended to remove humans from the process of collecting and analyzing data.

Examples of automated machine learning

Here are four examples of automated machine learning in action:

1. Sales and marketing software

When you complete an online form, your information is likely churned through a sales and/or marketing software. The software assigns a lead score to your form so the business can target you with specific messages based on the information you included in your form.

2. Health trends

When a large social service agency, like the Centers for Disease Control and Prevention, collects information from emergency rooms around the country, it uses software and algorithms to watch for patterns. It uses that data to inform health care providers, pharmaceutical companies and the public about things like new influenza strains or lung injuries associated with vaping.

3. Search engines

When you type a question into a search engine, like Google, the search engine collects information to generate results that answer your question. Search engines also use automated machine learning to deliver relevant ads to users.

4. Investment software

When investment managers make financial decisions for clients, they often use software or cloud-based applications to monitor markets and inform their predictions about gains and losses.

Data Scientists’ Roles in Automated Machine Learning

One of the myths surrounding automated machine learning and artificial intelligence is that they eliminate the need for data scientists. This couldn’t be further from the truth.

Data scientists play essential roles in AutoML, including:

Data cleansing (aka data cleaning) — Removing incomplete, incorrect, duplicate and corrupt records from a dataset
Feature selection — Choosing the variables that go into a dataset, which are used to develop a predictive model
Model selection — Selecting a machine learning approach, or mathematical representation of a real-world process
Parameter selection — Configuring a model’s variables
Critical analysis — Interpreting results and applying the knowledge to future decisions

How does AutoML differ from AI?

Automated machine learning and artificial intelligence aren’t synonymous terms. The difference between AutoML and AI comes down to what each does with the data it collects — AutoML generates reports from the data it collects, while AI uses the data to make decisions that humans would make.

Think of artificial intelligence as a sophisticated if-then formula. For example, AI can be used in a smart home when the owner connects thermostats, security cameras, smart speakers and other technology. Based on a series of data points, the devices can learn a family’s habits and “make decisions,” such as setting thermostats to different temperatures based on when the home is occupied versus empty.

SAS, an analytics software development company, does a nice job summing up the difference between automated machine learning and artificial intelligence:

“AI is the broad science of mimicking human abilities, and AutoML is a subset of AI that trains a machine how to learn.”

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