Machine Learning Statistics

Machine Learning Statistics image

Machine learning algorithms are taking over the world as we know it, however, in much more subtle ways than sci-fi movies would have us believe. That’s why we prepared these essential machine learning statistics to show you how this technology is transforming businesses and industries from healthcare to entertainment. Thanks to ML algorithms, firms can automate operations and perform tasks faster. So, read on and learn how you can benefit from it as well.

Machine Learning Statistics (Editor’s Choice)

  • Machine learning, NLP, and deep learning are the top three most in-demand skills on
  • A third of IT leaders want to use ML for business analytics.
  • Netflix saves up $1 billion per year thanks to machine learning.
  • The global ML market is projected to reach $8.81 billion by 2022.
  • By 2025, machines might replace 85 million jobs.
  • Machine learning takes up almost 60% of the AI investments outside the industry.
  • The global deep learning market’s worth is set to reach $93.34 billion by 2028.

General Machine Learning Statistics

1. Machine learning, NLP, and deep learning are the top three most in-demand skills on

Today, AI-related specializations and machine learning, in particular, are sought after by many companies. Stats further show that 98,000+ LinkedIn job listings mention machine learning as a must-have skill.


2. One-third of IT leaders want to use ML for business analytics.

IT leaders are betting on ML algorithms to boost their operations. A quarter of IT leaders would use it for security purposes, while 16% want to use machine learning for marketing and sales.


Leading Areas of Machine Learning Adoption Among Organizations Worldwide

3. Extracting better quality data is the top ML adoption driver for 60% of data scientists and C-level executives.

Machine learning statistics point to several factors behind its adoption. Increasing process productivity and speed is a driver for 48% of data analysts and C-level executives. For 46%, it’s simply about reducing costs. Finally, 31% want to extract more value from data.

(AI Multiple)

Leading Drivers of Machine Learning Adoption

4. Around 65% of organizations using or planning to use AI/ML, think that it will make for smarter, better-informed business decision-making.

Moreover, 74% of respondents believe it to be a game-changer, pointing to its potential to transform both the job and industry they work in. Out of those who already use these technologies, 58% said they ran models in production.


5. With 82%, risk management is the most common machine learning use case.

ML algorithms could be used for multiple purposes, and these vary from analytics to automation. Machine learning facts further show that other top uses include performance analysis and reporting (74%), trading (63%), and automation (61%).

(Finances Online)

Leading Machine Learning Use Cases

6. There were over 4.2 billion digital voice assistants powered by machine learning globally.

Forecasts suggest that those will double to 8.4 billion by 2024, or higher than the current global population. These virtual assistants are becoming an important part of the majority of consumer electronics devices. In the US alone, there are over 110 million virtual assistants.


7. With 43%, scaling up is the biggest challenge for machine learning adoption.

Despite it being a fast-growing segment of the technological world, machine learning facts show that it still faces multiple challenges. Versioning of ML models is next on the list of challenges with 41%, followed by getting organizational alignment and senior buy-in (34%). Other notable issues include cross programming language and framework support (33%) and the duplication of efforts across the organization (28%).

(Finances Online)

Leading Machine Learning Adoption Challenges

8. Around 61% of companies indicated in a survey that ML and AI are crucial data initiatives for the next year.

As many as 88% of respondents in the same survey noted their company either already had or has plans to implement AI/ML technology. Out of those planning implementation, 95% think it would either complement or make their job easier.


Interesting Machine Learning Facts & Stats

Machine Learning Facts & Stats

9. Netflix saves up $1 billion per year thanks to machine learning.

Out of the companies using machine learning, Netflix is one of the most notable examples. The streaming giant now drives 80% of viewers’ activity from its personalized recommendations, courtesy of its Netflix Recommendation Engine, which filters over 3,000 titles at a time using 1,300 recommendation clusters extracted from user preferences. NRE is estimated to be saving Netflix more than $1 billion a year.

(Lighthouse Labs)

10. Amazon’s click-to-ship cycle time dropped from 75 minutes to only 15 after the automation of picking and packing.

Machine learning statistics point to Amazon as another prominent example of how machine learning improves business operations. After acquiring Kiwa, a robotics company that helped improve the picking and packing times using machine learning, the ecommerce group’s operating costs fell 20%. At the same time, ROI rounded up to 40% of the original investment.


11. A study that used machine learning to predict the mortality of COVID-19 patients had 92% accuracy.

With the pandemic causing a shortage of medical resources, recent research applied machine learning algorithms to improve patient prognosis. The result was an ML-powered model which predicted mortality for patients based on medical information and sociodemographic data.


12. Google and Oxford researchers have created an ML system that reads lips from video better than humans with 46.8% precision.

Artificial intelligence and machine learning are applicable to areas you might not even expect them to be. So, Google’s machine learning system managed to outperform human lip-readers by a spectacular 12.4%, machine learning statistics show.


13. Amazon’s cashier-less stores earn 50% more revenue than regular ones.

Amazon is one of the big players in the machine learning market, as evidenced by its Amazon Go stores, where customers grab things from shelves and are charged upon exiting the store with the help of sensors and cameras relying heavily on ML algorithms. Analysts estimate that these stores might generate $4.5 billion in revenues by the end of 2021, significantly outperforming regular convenience stores.


14. MIT developed a system that helps identify 86% of cyberattacks.

Machine learning facts indicate that this technology has a significant application in the cybersecurity field. MIT’s AI laboratory has developed a system that can filter all potential attacks. The system saves time and reduces the error margin by reviewing data from tens of millions of logs. It sends out only filtered potential threats for further human evaluation.


15. Beth Israel Deaconess Medical Center in Boston managed to free up 30% of the operating room capacity with the help of machine learning.

One of the companies using machine learning to free up capacity is this famous medical center in Boston, according to machine learning stats. Using data from millions of patients and procedures done, disease info, gender, age, medication, and comorbidities, the implemented ML algorithm calculates the time required in the OR for each patient.

(The Enterprisers Project)

16. The AI model from Google managed to outperform six expert radiologists in detecting lung cancer.

The deep-learning-powered tool trained with a primary LDCT scan of patients in combination with an older LDCT scan where available. It managed to predict lung cancer better than six radiologists with 20 years of clinical experience.


Machine Learning Market Overview

17. The global ML market is projected to reach $8.81 billion by 2022.

Between 2016 and 2022, the global market for machine learning is set to grow at a tremendous CAGR of 44.1%. For reference, machine learning statistics put the market worth in 2016 at $1.03 billion. This growth isn’t unexpected, given the number of machine learning companies that have entered it in the last couple of years. Further, the adoption of ML solutions by organizations around the world is increasing ROI, enhancing customer experience, and driving firms ahead of their competition.


18. By 2025, machines might replace 85 million jobs.

The growth of the companies using machine learning and AI for their everyday operations has driven the changes in employment across industries. From manufacturing to customer service, workers are in danger from ML-powered software. On the upside, the technology will foster the creation of 97 million new jobs by the same period, AI and machine learning statistics show.


19. Around $28.5 billion in funding was allocated for machine learning applications in 2019 alone.

The machine learning market size keeps growing, fueled by AI funding. Next, $14 billion was assigned to machine learning platforms. Following that, $7 billion was allocated for small robots, natural language processing, and computer vision platforms, for each category. Finally, $4 billion was earmarked for recommendation engines and $3 billion for virtual assistants.

(Finances Online)

20. For 21% of respondents, AI/ML budgets grew up to 50% between 2018 and 2019.

The best sign of the machine learning market growth is the budgets companies allocate for this field. These vary across industries and different levels of organization’s maturity, yet most have witnessed a significant increase between 2018 and 2019, according to machine learning statistics. 43% of companies increased budgets by 1-25%, while 27% left them unchanged.


21. North America holds the biggest share of the machine learning market size, with 36.96% in terms of revenue.

The machine learning market has several large segments, North America, Europe, Asia Pacific, and the Rest of the world. North America, with the US at the top, is the market leader. A particular driver for its growth is the adoption of these solutions by both private and public organizations. Another market, recording a significant CAGR of 44.1% (2021-2029), is the Asia Pacific, according to machine learning statistics. In fact, it boasts the highest growth of all regions.

(PR Newswire)

22. The average salary of a data scientist in the US is $126,786.

Data science is one of the most common professions in the machine learning market and a lucrative one. The average hourly wage of a data scientist in the US is $65.20, and even entry-level positions could earn up to $99,917 annually. On the contrary, those experienced in the field are bound to earn upwards of $170,000, according to machine learning statistics.


23. Intel donated $1.5 million to fund the new research center for machine learning cybersecurity at the Georgia Institute of Technology.

Thanks to this donation, the researchers in this center will be able to study cybersecurity with a focus on the analytics used for malware detection. They will further analyze the vulnerabilities of ML algorithms and try to ramp up their resilience.

(Georgia Tech)

Deep Learning Statistics

24. The global deep learning market is set to reach $93.34 billion by 2028.

The adoption of cloud-based technology and the use of deep learning in big analytics are the main drivers. The market is projected to grow at a CAGR of 39.1% by 2028. Another important driver is the lack of need for a human programmer to tell it what to do. Instead, the large amounts of data are what’s pushing the technology forward.


25. Google developed a deep learning tool that helps identify metastasized breast cancer.

Deep learning statistics show that the tool boasts 99% precision. Detecting breast cancer in a phase where it has already spread from the primary zone to the nearby lymph nodes is a complicated process. Google researchers tried to simplify and enhance it using deep learning. As a result, they created a Lymph Node Assistant (Lyna) that managed to detect metastatic cancer on slides.


26. Just 16% of companies have gone beyond the piloting phase with deep learning.

Deep learning, as a form of machine learning, is not as present as ML. According to deep learning statistics, only a few companies have gone further than the initial stage. Out of those, 30% are telecom and high-tech outfits.


27. By 2020, it was estimated that 20% of companies would assign workers to monitor neural networks.

Neural networks that lie at the core of deep learning, require maintenance and monitoring. Despite the common understanding that these are products that start learning by themselves after some time, it doesn’t mean that human involvement is obsolete. These need to be retrained every time the data changes or new data is available.



While machine learning facts show how far the technology has come, researchers believe we are only at the infant stage of ML adoption. As you can see in these stats, it’s a fundamental part of AI, and its usage is shifting how businesses operate. Machine learning is growing at an enormous speed without showing signs of slowing down.

Machine Learning FAQ

Why is machine learning important?

Industries rely on ML algorithms to dissect large chunks of data and deliver more accurate results and usable datasets. Therefore, organizations are now able to make informed, data-driven decisions.

How is machine learning different from artificial intelligence?

Machine learning relies on the idea that programs and machines could learn through experience, machine learning facts show. AI meanwhile relies on machine learning algorithms, in a broader sense, and it applies various principles, not only ML. In a way, ML is a subset category of AI.

Are machine learning and deep learning the same?

Similar to machine learning, deep learning is also a sub-category of artificial intelligence. Where machine learning is used to parse data through algorithms, learn from it and make informed decisions, deep learning structures these algorithms. Deep learning statistics show that it creates a structure of algorithms more commonly known as the neural network. This network can learn and make intelligent decisions by itself.

Can machine learning predict the stock market?

AI and ML are being used frequently in the financial markets. By relying on big data and machine learning, researchers could improve online trading platforms. However, this works only for giving investors some edge and helping them recognize risks for the time being.

Will machine learning replace jobs?

It’s true, robots powered by machine learning can replace many human actions and jobs. Even today, many factories use ML-powered machines that enhance productivity but also work alongside people. Machine learning statistics show that 85 million jobs might be replaced by machines by 2025. However, the future is not so bleak for the workforce. The same stats indicate that 97 million new jobs will also emerge because of AI.

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