Machine learning (ML) has its roots in a paper published 80 years ago that attempted to use mathematical models to map human thought processes and decisions. This led to the development of the Turing Test that determined whether or not a computer could think like a person.
In the decades since then, machines have revolutionized how businesses solve problems to increase efficiencies, reduce costs, gain market share and drive profits. Smart machines also have created thousands of more career opportunities as companies across the economic spectrum continue to invest billions of dollars into the light-speed adoption of ML and artificial intelligence (AI) technologies.
The future of ML is promising, and businesses that don’t invest in ML will fall behind. According to itransition, global spending on ML/AI will grow to $1.6 trillion in 2030 from $500 billion in 2023, a Compound Annual Growth Rate (CAGR) of 38%. Forty-nine percent of recently surveyed business leaders said ML and AI deployment is a high priority and nearly one-third of those ranked it No. 1. Additionally, by 2025, almost all enterprises will be using some form of ML/AI.
However, despite the money pouring into deploying ML/AI technologies, hitting those goals may be in jeopardy: only 12% of recently surveyed companies say there is an adequate supply of technology talent.
“This shortage has the potential to hold back digital innovation and economic growth,” itransition warns, citing an IBM index that found “34% of organizations consider insufficient AI skills, expertise or knowledge as the top reason blocking successful AI adoption.”
How Are Industries Using ML to Solve Problems?
La Salle University’s online Master of Science in Computer Information Science with a Specialization in Data Science program equips professionals with the ML and problem-solving skills to help industries use ML technology.
Rapyder declares that ML’s capacity to teach itself to identify trends and anomalies as it encounters new data is among “the most disruptive technologies we have ever encountered.” The ability to transform large, complex data sets into actionable intelligence enables leaders to model and evaluate potential solutions quickly and make decisions with greater certainty.
Industries that are leading the adoption of ML include:
- Finance: embraces ML to detect fraud, use computer algorithms to identify trading opportunities and assess risk
- Healthcare: applies ML to assist in diagnosing diseases, researching and developing pharmaceuticals and personalizing doctor-patient relationships
- E-commerce: leverages data-based intelligence to recommend purchases to consumers, segment customers by demographics and psychographics and enable dynamic pricing
- Manufacturing: deploys ML to predict maintenance schedules, control and assure quality and optimize supply chain operations
- Marketing and advertising: use ML to gain efficiencies in advertising, analyze market conditions and predict customer behavior
- Autonomous vehicles and transportation: use ML for route planning (Uber, for instance, uses it to scale pricing to demand), detecting objects, initiating braking to avoid collisions and self-navigation
In order to take advantage of these benefits, though, professionals must have the necessary skills. While ML produces “positive business outcomes,” MIT notes, “it not fairy dust … To realize its potential, we have to help people understand what machine learning is and what it isn’t, and how they can best apply it to their business.”
What Are Some Technology Careers in ML/AI Deployment?
Graduates of La Salle’s program have several potential jobs they can pursue with their skill sets. Professionals who use ML/AI will possess computer, data science, programming and problem-solving skills for roles such as:
- Software developers design and implement ML algorithms, prototype models and write code. Other responsibilities can include optimizing code, scaling ML and assuring the quality of system performance and reliability. The professional can earn up to $109,020 a year, according to the S. Bureau of Labor Statistics (BLS).
- Business analysts use data-based insights to predict and track costs, revenue, trends and margins. Overall, ML enables them to allocate resources more efficiently, often by identifying routine tasks that can be automated. These professionals can earn up to $83,950 annually (per BLS per Comparably).
- Information analysts leverage ML algorithms and techniques to classify and cluster data, identify trends, patterns and anomalies. Those understandings, in turn, inform strategic business decisions that improve the workflow of information analytics. According to BLS, information security analysts can make up to $102,600 a year.
“ML skills will be in demand for decades to come,” according to Career Karma. “Employers offer a hefty annual salary to attract the best in the industry, indicating that ML is an excellent career path.”
How Do Professionals Acquire Skills for ML-related Careers?
La Salle University’s Master of Science in Computer Information Science with a Specialization in Data Science online program equips graduates with the expertise and insights to drive technical solutions to real-world business problems.
The accredited curriculum focuses on developing skills to define problems and create, implement and test technical solutions. The concentration on data science explores integrating database services and enterprise and web solutions. Relevant courses that emphasize skills in these areas include Data Mining and Artificial Intelligence courses.
Learn more about the La Salle University’s online Master of Science in Computer Information Science with a Specialization in Data Science program.