Customer Data & Analytics Blog

AutoML could help bridge the AI skills gap

Janet Wagner | 2 minute read

Zylotech_AutoML Could Help Bridge the AI Skills Gap_030719_headerThe number of companies currently using AI is growing rapidly along with the companies that want to start leveraging AI. But the shortage of AI talent is a significant problem for companies looking to implement AI technologies.

AI talent shortage

According to a New York Times article, the number of professionals with the skills needed to perform significant AI research is less than 10,000. AI requires a diverse set of skills which includes advanced math, statistics, and coding. AI also covers a wide range of jobs including data scientists, engineers, and software developers.

The demand for people with AI skills is growing, especially at major tech companies like Amazon, Google, and Microsoft. AI talent is so scarce that some tech companies are hiring AI professors from universities and recruiting AI PhDs before they’ve graduated. And it takes years for a university to train a PhD in the field of AI. So, academia is losing AI professors and PhDs faster than they can be replaced.

AI professionals command high salaries

Major tech companies are attracting and retaining AI talent by offering high salaries- even a moderately skilled AI professional can earn hundreds of thousands of dollars per year. Some AI Zylotech_AutoML Could Help Bridge the AI Skills Gap_030719_subexperts earn more than a million dollars per year. Big tech companies have the revenue to attract and retain highly skilled AI professionals. And major tech companies are cornering the market when it comes to AI talent. Not many companies have a Google or Microsoft-sized AI budget though. So, for companies with modest budgets, the AI skills gap is a more significant problem.

AutoML could help bridge the AI skills gap

AutoML could help bridge the AI skills gap by allowing companies to boost the productivity of their data science and engineering teams. The jobs in AI often require spending a great deal of time collecting, cleaning, and preparing data. For data scientists, this data is used for training algorithms and building models. Data scientists must also spend time on feature engineering, feature selection, and validating models. AutoML allows many of these repetitive tasks to be automated. With much of the model building process automated, data science teams can build models in less time and with greater accuracy.

Zylotech leverages AutoML

The analytics layer of our customer data platform (CDP) is powered by AutoML. And we’ve designed our platform to automate the entire customer analytics process- from aggregating multiple sources of customer data to enabling machine learning-driven customer analytics. Our platform provides data scientists cleaned and standardized marketing data sets. Data scientists don’t have to deal with duplicate records or figure out if similar records are related. Our platform does the data prep work automatically, so data scientists don’t have to.

Lack of AI talent is a long-term problem

Until academia produces enough AI talent to keep up with the demand, the AI skills gap is not going away anytime soon. Organizations must find ways to make the most out of the AI talent they have.

Janet Wagner is a Zylotech contributing writer.

If you liked this post, check out our recent blog post: K-Means: a popular algorithm for segmentation.