Machine learning has become an increasingly important and in-demand field in the United States in recent years. There are several key factors that have contributed to the rise in demand for machine learning skills and professionals:
Growth of Big Data
One of the main drivers of demand for machine learning is the rapid growth in the availability and importance of big data. As organizations across industries gather and analyze massive datasets, they need advanced analytical techniques like machine learning to uncover patterns, derive insights, and make predictions.
According to International Data Corporation forecasts, the amount of digital data created in the world is expected to grow from 33 zettabytes in 2018 to a staggering 175 zettabytes by 2025. Dealing with such huge and complex data requires sophisticated machine learning capabilities.
Need for Automation
Another key factor is the increasing need for automation in fields like manufacturing, customer service, finance, healthcare, and more. Machine learning algorithms enable systems to automatically learn from data and improve at tasks without being explicitly programmed.
This makes machine learning ideal for automating tasks that are difficult to code with explicit rules – for example, speech recognition, visual inspection in manufacturing, anomaly detection in fraud prevention, and disease diagnosis based on medical images.
As the appetite for automation grows, fueled by things like robotics and IoT devices, demand for machine learning skills rises in tandem.
Advancements in Algorithms
In recent years, there have been rapid advancements in machine learning algorithms and techniques, enabling new and expanded applications. Areas like deep learning, neural networks, natural language processing, and reinforcement learning have made great strides.
As the power and capabilities of machine learning improves, it is being adopted more widely, driving up the need for people with relevant skills and experience.
Competitive Advantage
Additionally, machine learning capabilities provide a competitive edge for companies. Early adopters of machine learning have been able to achieve greater efficiencies, better engage customers, uncover new revenue opportunities, and more.
Seeing these proven benefits, more businesses are implementing machine learning to stay competitive. But this requires hiring scarce and expensive machine learning talent.
Shortage of Qualified Workers
While demand has increased rapidly, the supply of qualified machine learning experts has not kept up. According to a report by Indeed, the demand for professionals with machine learning skills has grown by 344% since 2015, while searches for these roles only increased by 1.5 times.
Some key stats on the talent shortage:
- There are over 800,000 active job openings requiring machine learning skills across various industries.
- Only 22% of machine learning roles are filled within 60 days – much lower than the 45% average for other IT jobs.
- It takes over 45 days on average to fill a machine learning position.
This significant gap between demand and qualified talent adds to the strong demand for those with training in machine learning.
High Compensation
The high demand and shortage of talent has led to very lucrative salaries for machine learning roles. According to Glassdoor, the average base pay for a machine learning engineer in the US is over $146,000 per year.
Some key compensation stats:
Role | Average Base Salary |
---|---|
Machine Learning Engineer | $146,744 |
Data Scientist | $120,931 |
AI Research Scientist | $132,066 |
These sizable salaries, often accompanied by other incentives like stock options at tech companies, further motivate professionals to pursue careers in machine learning.
Increased Adoption Across Industries
While tech companies are seen as the pioneers in adopting machine learning, its use has expanded to many other sectors like:
- Finance – Algorithmic trading, fraud detection, risk assessment.
- Manufacturing – Predictive maintenance, production optimization, quality control.
- Healthcare – Clinical diagnosis, drug discovery, image analysis.
- Automotive – Self-driving vehicles, intelligent transportation systems.
- Retail – Recommendation systems, inventory management, customer segmentation.
- Media – Personalized content, speech/face recognition, targeted advertising.
As more industries adopt machine learning for a variety of applications, demand grows for experts across different domains.
Government Investments
Attracted by the transformative potential of machine learning in areas like defense, intelligence, space exploration, and more, government agencies are aggressively investing in machine learning research and talent.
For example, the US Department of Defense requested a budget of $927 million for AI and machine learning in 2022. Government funding for academic research in machine learning has also increased substantially in recent years.
These investments stimulate advancements in machine learning while also developing talent in the field.
New Job Roles
As machine learning becomes mainstream, new related job roles are emerging across industries. Some of these new titles include:
- Machine Learning Engineer
- Data Scientist
- Business Intelligence Developer
- Deep Learning Engineer
- AI Researcher
- Machine Learning Operations (MLOps) Engineer
- Applied Machine Learning Scientist
- Machine Learning Ops Manager
These specialized roles are highly sought after and receive premium salaries due to very high demand, contributing to the attractiveness of the field.
Investments in Startups
Venture capital funding and investments into AI and machine learning startups has boomed in recent years. In 2021 alone, over $54 billion of private investment poured into AI and machine learning companies globally.
Some top funded companies in this space include Databricks, DataRobot, Scale AI, etc. The growth in funding mirrors the growing market demand for machine learning capabilities.
All this startup activity and funding availability incentivizes tech talent to enter the domain of machine learning, which drives up demand further.
Expansion of Higher Education Programs
Responding to the surging interest in entering the machine learning field, many universities have launched new educational programs, certifications, and research centers focused on machine learning and AI.
For example, leading universities like MIT, Stanford, and Carnegie Mellon offer specialized master’s programs in machine learning and data science. Free online courses on machine learning topics are also hugely popular on platforms like Coursera and Udacity.
This expansion in formal education avenues is essential for training the next generation of machine learning experts.
Adoption of Cloud-based ML Tools
The development of user-friendly, cloud-based machine learning platforms by companies like Google, Amazon, and Microsoft has also accelerated mainstream adoption of machine learning.
These platforms include services like:
- Google Cloud AI
- Amazon SageMaker
- Microsoft Azure Machine Learning
- IBM Watson Studio
With these cloud tools, companies can more easily apply machine learning to their data and problems without needing to develop extensive in-house capabilities. But they still need machine learning professionals to operate and optimize these platforms.
Growth of ML Research
There has been an exponential rise in published scientific research papers on machine learning in the past decade, according to the IEEE. Advancing the capabilities of machine learning requires extensive research efforts, often funded by government agencies and tech companies.
This ever-growing research output further pushes progress in the field while producing more PhDs and researchers focused on machine learning.
Number of Published ML Papers Per Year
Year | Number of Papers |
---|---|
1996 | 2,157 |
2000 | 5,007 |
2005 | 12,895 |
2010 | 32,689 |
2015 | 95,080 |
2020 | 169,986 |
Growth of ML Conferences
Parallel to the rise of research output, the number of international conferences focused on machine learning has also grown substantially. Major conferences like NeurIPS, ICML, ICLR, CVPR and others see ever-growing attendance.
These conferences allow researchers and practitioners to share cutting-edge work and recruit talent. Their expansion highlights the momentum in the machine learning space.
Conclusion
In summary, the convergence of growing data, need for automation, major technology advancements, strong commercial incentives, widespread industrial adoption, increased investments and education, and thriving research has created surging demand for machine learning skills and professionals in the United States.
This trend is expected to continue as machine learning capabilities improve and are applied to even more fields. Any individual looking for an exciting and promising career path would do well to develop abilities in this area.