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There’s no doubt about it; automated machine learning (AutoML) has exploded in the Data Science field, revolutionizing how data interactions work. And companies have caught on. Businesses left and right are implementing automated machine learning models to leverage advanced analytics, enhance revenue, and enrich customer experiences.

But what exactly is AutoML, and how does it help companies? In this blog post, you’ll learn everything there is to know about automated machine learning, as well as how to leverage it to your business’s advantage.

What is automated machine learning?

Automated machine learning is the process of automating time-intensive, repetitive tasks of machine learning model development. It serves to automate data preparation and modeling and tuning steps so that manual data mining is a thing of the past.

Companies have been quick to adopt automated machine learning models in the workplace since it dramatically improves efficiency and productivity. For example, in the telecom industry, AutoML is used to create customer churn models that foretell which customers have a higher risk of canceling contracts.

Overall, a key advantage of embracing AutoML is that it reduces the expertise gap between technical specialists and stakeholders working with business data science, reducing the time and niche skills required to create models.

The difference between AutoML and machine learning

Automated machine learning and machine learning are two terms that get tossed together but they’re not the same thing. Machine learning helps models or algorithms learn based on the data presented. Meanwhile, AutoML is when the algorithm or model is automatized, streamlining data preparation and model selection.

Evolution of automated machine learning

While automated machine learning isn’t a new concept by any means, it’s evolving at lightning speed. It’s easy to understand why. Automated machine learning tools drive a new way of thinking about data science, aiming to alleviate the burden on data scientists alone to do their jobs. For instance, energy companies today hugely benefit from AutoML, as they’re able to predict customer cancellations ahead of time just by introducing a line of code.

It’s also worth noting, AutoML isn’t here to steal jobs, nor can it solve every data-science challenge. Rather, the technology can be used to streamline the doing of typical forecasting tasks, where the goal is to predict an outcome. Models that require statistical expertise are still best left to humans to tend to.

How AutoML is supporting business growth

Experts predict that AutoML models will double in the workplace in the next five years, decreasing the exclusive demand for data scientists. In fact, companies are already building their human resources capacities around AutoML. Below are five industry areas that are forecasted to embrace machine learning and grow their profit margins.

Customer care

It’s virtually every company’s best interest to improve their customer service while keeping costs down. And with AutoML, it’s possible. Take chatbots, for instance. They’re helping companies solve customer care issues at record speed and subsequently improve the overall customer experience. Of course, there’s still a need for employees to step in occasionally. Still, the chatbots help alleviate this burden on humans alone to solve issues, redirecting their focus to other, more pressing tasks at hand.

Customer loyalty and retention

In today’s competitive business landscape, customer loyalty and retention are everything. Thanks to AutoML, companies can now mine customer actions and transactions, collecting data that pinpoint who’s at risk of leaving. This helps companies strategize and mitigate the risks of losing customers.

Human resources

It’s a famous saying that candidate sourcing is the most difficult part of a recruiter’s role. Scouting through hundreds of upon hundreds of applications for the perfect candidate takes time, effort, and willpower. Automated machine learning software can sift through applications in seconds, picking out the applications that shine while disqualifying the rest.

Detecting fraud

Safeguarding business interests against risk is a critical component of business continuity today. With machine learning, models can step in to identify anomalies, exceptions, and outliers in data transactions in real-time. This helps protect companies against fraud, and other security threats like tax evasion, for example.

Marketing automation

AutoML in marketing helps companies segment marketing campaigns, track campaign performance, and ultimately nurture and convert quality leads. AutoML models offer invaluable insight into customer behaviors and interactions, helping companies maintain a handle on their marketing operations.

How to get started with AutoML

Ready to leap forward and embrace the powers of automated machine learning? Here are a few tips to help get you started.

1. Assess your needs

A starting point would be to figure out what data can be left up to the data scientists to handle and what tasks could be automized. Don’t underestimate the complexities of data. It’s best to work alongside an experienced analyst who can determine what’s possible.

2. Understand the pros and cons

AutoML is a fantastic way to streamline your data mining processes and save operational costs in the long run, but it has its faults. It can be costly to implement at work, and you might get some resistance from existing staff members. It’s also worth noting that some talent can’t be replaced, so it’s crucial you grasp how AutoML software can fill a talent gap at work, as well as understand what it can’t do.

3. Explore a good automation platform

Finding the automation platform for your business needs is pivotal to your success. Do your homework and look at the pros and cons of each shortlisted automation platform.

4. Upskill your staff

A cost-effective, efficient way to get started with automated machine learning software is to train your existing team on how it works rather than hiring fresh staff. When your team gets comfortable with AutoML tools, it’ll be much easier to roll out a company-wide policy on its uses.

Final word

While there’s no way of knowing just how far AutoML will go in automating data-driven assignments, total automation seems far-fetched at this stage. What’s for sure is that automated machine learning is an incredible way to free up your employees’ time to do more important tasks, increase business profits, as well as ultimately grow your company, turning it into a cutting-edge, solutions-oriented business.

Got questions? DeepIdea Lab is here to help. With years of experience delivering custom-made automated solutions to businesses in need, we can help identify the areas your business needs automating and then automate them.

Surya Sanchez

Founder of DeepIdea Lab. A digital agency focused on automation.