Numerous potential benefits exist if you’re thinking about using automated machine learning (AutoML) for your business. Many leaders also consider AutoML an option for breaking down barriers that would otherwise make it difficult or impossible to deploy machine learning.
1. It Can Reduce Time Frames
One of the reasons why people find AutoML platforms and tools so appealing is that they eliminate some of the most time-consuming steps. For example, you can skip labeling your data and building a predictive model because AutoML will do it for you.
This allows you to spend more time using machine learning to solve business problems instead of getting caught up in logistics. That can be especially useful if you’re interested but only have a small team or other resource limitations. A related benefit is that the shorter time needed to develop machine learning models lets you create more of them than previously planned.
Even so, it’s good to have a clear idea of what initially sparked your interest in machine learning. What business challenges do you want to solve by using it? How could it help you improve operations and gain insights?
“Automated machine learning can be a significant time-saver, allowing you to bypass many model-creation steps”
2. It Does Not Require Extensive Technical Expertise
People are also excited about AutoML’s potential because it allows companies to use machine learning without necessarily hiring data scientists first. A 2022 survey highlighted some of the impacts of the persistent talent shortage within the data science sector.
In the study, 64% of respondents ranked the need to recruit and train technical talent as their top concern. Another 56% of those polled believed insufficient talent and headcount are some of the main issues restricting enterprise-level data science adoption.
Many AutoML are low- and no-code options, opening possibilities for users without strong technical backgrounds. It’s no surprise that some people say AutoML could be a game-changer because it democratizes machine learning. Profet AI’s AutoML Virtual Data Scientist Platform allows people to build predictive models with only basic machine learning knowledge.
3. It Minimizes Manual Effort and Increases Trust
Hand-coding machine learning models is challenging, even for the most conscientious people. Doing this by hand increases the possibility of errors, and employees risk missing deadlines for their machine-learning projects or failing to spot functionality issues.
AutoML does not eliminate error potential, but it lowers the threat by freeing people from detail-oriented duties that could lead to costly mistakes. Bringing automation to machine learning model creation can also foster trust in the outcome.
Technology helps people automate many of their controls, making businesses more resilient. For example, many banks use machine learning to detect fraudulent transactions. That control mechanism protects consumers and their banking institutions. However, people will be most confident in using machine learning if they believe it’s reliable and error-free. AutoML increases the chances of models having those characteristics.
“Today’s business representatives are highly concerned about the challenges of recruiting data science talent. AutoML can alleviate some of them by making model production more manageable.”
4. It Accelerates the Time-to-Value Ratio
Company leaders understandably want to know how long it’ll take for their machine-learning investments to pay off in meaningful ways. There’s no universal answer, but people who use AutoML can anticipate that it will speed up the time required to see the measurable value.
One of the reasons is that it brings more standardization to the model-creation process. Once people figure out what works, they can replicate it across other projects. Many individuals building machine learning models often need help determining when they’re accurate enough to move into the production phase. AutoML tools can handle that step, removing some of the guesswork that could otherwise stifle deployment.
Executives often balk at investing in new technologies because they’re worried about waiting years to see the results. However, AutoML helps people embrace machine learning without all the aspects that can cause project bottlenecks.
5. It Enhances Workflows
Before automated machine learning was available, it could take data scientists hours to build their models. Now, they only need 15 minutes to get comparable results. It’s not always feasible for a single person to spend hours on a single task, even if it is one as important as building a machine learning model.
In addition to the overall improved productivity for development, the more efficient workflows associated with AutoML allow people from more areas of the business to get involved and provide feedback about model creation and usage.
Instead of machine learning being a concern for a single department or team, everyone can play a vital role in how technology affects an organization. Getting feedback from others enables steering clear of pitfalls and building models that are most likely to achieve the expected results.
“AutoML prevents having to hand-code machine learning models, which can make them more reliable and increase people’s trust in them.”
That doesn’t mean all AutoML models will work perfectly on the first try. However, the automated elements of the model-building process help people spend more time and other resources on the big-picture ideas that matter most.
Are You Ready to Use Automated Machine Learning?
AutoML may not be a solution your company deploys immediately, and that’s OK. However, these five benefits show why more companies are using it and getting excellent results.