What Is Automated Machine Learning and How Could It Help You?

Automated machine learning, or AutoML, centers on bringing automation to various stages of the machine learning process. It can dramatically improve the workflow, including reducing the number of machine learning experts a company needs on its team to get the desired results. Let’s explore the potential of AutoML and how businesses can use it. 

How Can AutoML Improve Machine Learning Model Creation?

Building a machine learning model is often a labor-intensive process. One of the main goals of AutoML tools is to reduce the work required to build high-performing models customized for an organization’s needs. Minimizing the labor-related barriers could make ML more appealing to organizational leaders who want to start using the technology but worry about the associated workloads. 

Mike Gualtieri, a vice president and principal analyst at Forrester Research, discussed AutoML’s potential in a 2019 interview. He began by clarifying what it does and does not do. AutoML products typically don’t handle data acquisition. That means people must figure out what data they want to use before constructing their models. However, they can start using automated machine learning products for data preparation, ensuring high-quality information.

From there, AutoML tools can assist with feature engineering. That step involves turning raw data into features that inform how the model will function. After that comes the typically intensive stage of evaluating various models and their data to determine which will perform the best for the particular needs. 

It’s in this part of the pipeline that automated machine learning really shines. That’s because it can combine several steps within this stage, allowing people to evaluate models much faster than they otherwise would. 

Which Industries Use AutoML?

Applying automated machine learning to business use cases is still in the relatively early stages. However, significant growth is already happening in the market. It had a total worth of $346.2 million in 2020. Analysts believed it would show a 55.6% compound annual growth rate from 2020 to 2030. 

The report connected the significant growth of automated machine learning to the increasing use of artificial intelligence in a broader sense, including how companies deploy AI chatbots and use artificial intelligence for improved data analysis. 

The team behind the analysis expected the sales and marketing management and health care industries to see the highest growth rate during the studied period. Then, the report mentioned some applications people would target while using automated machine learning products. Those included providing personalized product recommendations, making fraud detection solutions work more efficiently and creating predictive lead-scoring tools.

However, these are only some of the ways to deploy the technology. People interested in using AutoML should start by thinking about how machine learning could help their business. After that, they can narrow their research efforts by exploring the automated machine learning solutions on the market. They should assess whether AutoML would target some of the obstacles that currently make it difficult or impossible for them to deploy machine learning.

What Are the Main Advantages of Automated Machine Learning?

Knowing what to expect before deploying an AutoML product can help people anticipate the likely advantages they’ll enjoy. Here are some of them: 

Tackling the Machine Learning Talent Shortage

A 2022 study from SAS found that 63% of business leaders from various industries indicated their biggest skills shortages were in artificial intelligence and machine learning. AutoML solutions don’t solve that lack of talent, but they begin addressing it. Investing in a robust and feature-filled tool to automate some machine learning aspects could allow team members to get more done without expanding an existing team. 

The important thing to remember is that AutoML is not a replacement for people who bring their skills to machine learning projects. Instead, it can save time and combine steps, making those individuals more productive. 

Enabling a Shorter Time-to-Value Metric

Some sources say that AutoML’s primary benefit is that it makes machine learning models valuable more quickly for the businesses using them. Many company leaders are understandably sharply focused on how long it takes for machine learning to provide a return on investment.

Since automated machine learning aids people in getting models ready for deployment more efficiently, it takes less time to see how using the technology will positively affect the business. Automated machine learning could also complement other efforts to boost efficiency. For example, active machine learning involves a model choosing the data that will teach it. Improved efficiency is one of the associated benefits because this approach only selects the data points that will bring the best results. 

Allowing Faster Data Analysis

Getting quick but accurate results from machine learning algorithms is becoming increasingly important as people use them to diagnose ailments or decide whether to grant loans. When a model’s decision could change someone’s life, the results must be as trustworthy as possible.

In one research example that used automated machine learning, a team found that it only took the technology four seconds to check an MRI of the heart. In contrast, technicians usually need 13 minutes to analyze someone’s heart function with an MRI. 

The work occurred in the United Kingdom. Associated estimates showed that using AutoML to examine heart MRIs could save 54 clinician days per year at each facility relying on it. Assessments to compare the machine learning model’s performance to humans showed no significant differences between the two. 

Promoting Better Sustainability 

As technologies progress, it sometimes becomes apparent that they’re not necessarily very sustainable. The energy usage associated with data centers is a good example of such a problem, although people are making various positive changes for the better. 

Training and deploying some kinds of neural networks can be incredibly power and emissions-intensive. That reality led MIT researchers to see if they could use automated machine learning to make much-needed improvements. They developed what they termed a “once-for-all network.” It trains a single large neural network. It consists of numerous smaller and pretrained subnetworks that people can use to get the models ready for various platforms and projects. 

Estimates made during experiments indicated this system used 1/1,300 of the carbon emissions usually associated with training a computer-vision model. Such progress could lead to lasting changes in the wider industry if this approach proves successful at scale. 

Are You Ready to Use Automated Machine Learning?

Many people believe AutoML could democratize machine learning by removing some of the barriers that make it difficult or impossible for companies to use it with limited resources. It takes time and forethought to determine the most appropriate and effective ways to use automated machine learning in an organization. However, the examples here show how businesses taking this route could reap rewards.