by Stephen Zafarino

The outlook for machine learning in tech: ML and AI skills in high demand

Jul 27, 20185 mins
Artificial IntelligenceIT SkillsMachine Learning

Discover the latest projections for the future of machine learning in tech, including the mainstream adoption of NLP and voice-driven interactions and the rise of innovative ML/AI talent development initiatives.

Artificial intelligence (AI) is proving to be the most significant technological advancement across all industries in recent decades. While we’re still years away from the robotics side of AI, the machine learning (ML) sector has exploded by helping companies with everything from improving customer retention rates to driving enhanced insights from big data and even mitigating supply chain risks.

With the global machine learning market anticipated to grow from $1.4B in 2017 to $8.8B by 2022 according to a recent report by Research and Markets, here’s a look at where those investments are headed and what it means for increasingly in-demand machine learning talent.

Say hello to NLP and voice-driven interactions

Last year, Amazon introduced us all to Alexa in the workplace, but this voice-activated, AI-powered device is only the beginning. Natural language processing (NLP), made possible through machine learning, helps computers, systems, and solutions better understand the context and meaning of sentences. As NLP is refined and improved, humans will communicate with machines seamlessly solely through voice without needing to write code for a command.

Today’s NLP and voice-driven interactions with machines are focused mostly on mundane or tedious tasks, like organizing your schedule or providing simple data sets. However, expect the skills of these IoT devices to evolve dramatically in the coming years as they gain mainstream adoption in the workplace and developers actively create both public and private ML-powered applications currently not possible today. Leading innovators and leaders in the voice-activated device market — Google and Amazon — will uncover a wide variety of new uses for these devices as investments in research and development of ML and NLP increase. 

Machine learning to remain most in-demand AI skill

Machine learning today is used for image identification and classification at scale, consumer-driven chatbots, NLP and voice search, lead prediction, and more advanced neural networks, like Google’s DeepMind network. The applications for and uses of machine learning are just now becoming realized, but employer demand for talent with ML skills has already risen dramatically.

Over the past three years alone the number of AI-related job postings on Indeed has increased by 119 percent, according to the platform’s latest AI talent report. The machine learning engineer role was cited as the third most in-demand AI job of the moment with machine learning ranked as the most in-demand AI skill. With AI projected to create 2.3 million jobs by 2020, according to a Gartner report, I think it’s safe to assume that machine learning will remain an in-demand skill for the foreseeable future.

High-end ML talent will become increasingly limited

The widespread tech talent shortage will impact machine learning talent, specifically, in the months and years to come. While demand for talent with AI skills has doubled over the past three years, the same Indeed report notes a plateau of searches from job-seekers for AI-related roles beginning in early 2017. Despite increased interest from employers for AI skills, job-seeker interest in AI-related roles seems to be leveling off.

As demand continues to outpace the supply of qualified talent for these emerging skills, recruitment efforts for ML skills will become even more competitive. Talent with AI and ML skills already command top salaries, with machine learning engineers netting an average salary range of $125,000 to $175,000. But employers looking to hire this skill set should expect to offer salaries on the higher end of that range, or even higher, as demand increases. Benefits packages from employers looking to land top talent will also cater to the perks this talent group values most, like WFH/remote work options, flexible scheduling, extended PTO plans, and professional growth investments and opportunities. 

The rise of innovative ML talent development initiatives

Available machine learning talent is finite, something companies looking to hire for this skill set today know all too well. In response to the limited talent available and a current drop in interest from job-seekers for AI-related roles, we’re witnessing a rise in innovative talent development initiatives from companies looking to supply their business with the necessary talent they need now and in the future.

Tech giants like Google are doing their part by making skill development resources available, like their recently published and entirely free 3-month course on deep learning, in an effort to spur today’s students and active job-seekers to consider a career in an AI-related field. Other companies are developing training programs and AI-focused schools both  in the U.S. and internationally, focused on retraining grads in math and physics to develop ML skills to funnel qualified talent directly from these programs to employment.

For both SMBs and enterprise-level organizations desperate for ML and AI talent, the key to netting skilled talent will be direct collaboration with basecamps, universities, training programs, and other educational organizations on innovative initiatives to help tomorrow’s talent develop skills specifically tailored to meet the market’s needs.

As AI continues to transform the way we interact with the world and the machines we are increasingly reliant on, we’ll see more and more futuristic predictions coming true. Among them will be the mainstream adoption of NLP and voice-activated interactions, increased demand for machine learning skills, an increasingly limited talent pool, and a rise in innovative talent development initiatives from employers and educational organizations collaborating to resolve the ongoing AI talent shortage.