Machine learning is now changing how businesses handle data. Whether it is decision-making or programming, the machine learning industry is the overarching theme among many other industries. As one of the branches of artificial intelligence, uses of machine learning will reach $97 billion in the next three years only.
The future of business is now depending on the agility and automation of the machine learning industry. Artificial intelligence in business has allowed it to incorporate prediction analysis, understanding of data patterns and speech recognition, etc into their daily processes. Other uses of machine learning include automating communication, data mining, data analysis, and cyber security. An intriguing way in which the machine learning industry has attracted many sectors is business automation. We have shared more about the different uses of machine learning below.
Digitization of the industrial sector has brought the benefits of deep learning, increased efficiencies, better productivity, automation in business processes, and efficient decision-making. This development stands true for all industries and this article will explore how the uses of machine learning are implemented in each area. But first, we will take a look at the different ways machine learning is used.
Different Uses of Machine Learning
Here are different ways machine learning is used in businesses every day:
- Machine learning has been valuable to businesses when it comes to managing communication channels like automating customer channels, sales pitches, customer support with AI bots, etc
- The most relevant use of machine learning is the use of artificial intelligence to mine data and find patterns in order to present analytics
- Another important factor is the use of machine learning to present predictive analysis in order to improve and smoothen the decision-making process. Predicting outcomes is one of the widespread ways machine learning is used.
Top ten sectors for the machine learning industry you need to know about:
- Healthcare
AI-based algorithms are used for developing treatment plans for patients based on large amounts of research data. Machine learning is also used for making a quicker diagnosis for patients based on their history, samples, previous medication, etc. There are also other applications that help hospitals with managing operational processes like patients, their data, finance management, employee schedules, etc.
- Ecommerce
The eCommerce industry is immensely impacted by the use of machine learning and how artificial intelligence is involved in everyday aspects of running businesses. Ecommerce sales are now skyrocketing for businesses because machine learning applications can predict consumer behavior to see what inventory needs to be updated.
Moreover, uses of machine learning also involve the use of forecasts to predict market trends so businesses are thrown a curve ball when trends change every season. The best examples of how machine learning is used are personal consumer recommendations which streaming services like Netflix use to cater to each consumer as per their taste. Even Amazon and many brands offer customized wish lists based on old purchases and search history through their website.
- Real estate
The real estate industry is booming thanks to machine learning where homes and commercial properties are managed in a database. This is done to keep track of the crime rate and environmental indicators of different neighborhoods. Moreover, other parameters of properties are also gauged and stored which include mortgage rates, future valuation, etc. The real estate industry is also using machine learning to develop applications in order to smoothen the process of people finding their desired properties.
- Banking and finance sector
Other than eCommerce, the stock market also benefits from reinforced learning algorithms in order to predict market behavior. When it comes to trading, analyzing company performances to invest is also a big trend due to the use of machine learning. Moreover, the banking sector is using machine learning to analyze the lenders’ ability for loan repayment. Although data analysis was used before in the banking sector, machine learning offers even more accuracy and swift results. Fintech applications are also developed to help users navigate which investments are better for them as well as manage their personal expenses.
- Entertainment industry
A great example of how the entertainment industry is using machine learning is the OTT platform using reinforced learning to show recommendations to their users based on their previous searches. This is pretty similar to how commerce websites send recommended product lists to their users who have shopped previously or simply browsed their online stores.
The entertainment industry is also using another form of machine learning which is virtual reality. This sort of technology is used in video games as well as movies to develop simulation effects. However, the impact of virtual reality goes beyond just the entertainment industry. This technology is also used in medicine to do surgical procedures with minute precision and in educational institutes to generate content with 3D simulation.
- Education sector
The education sector is benefiting from the virtues of machine learning by developing teaching methods based on the curriculum. It also helps educationists to develop methods for teaching depending on their student’s engagement levels and attention span. Moreover, machine learning is also used for assessing exams automatically, assessing the performances of students, and then making better decisions regarding their teaching schedule.
- Cybersecurity
Machine learning is deeply embedded in cyber security. With machine learning, attacks can be prevented and also reduce the amount of time needed to respond in the case of an attack. The use of data is essential in cyber security because machine learning uses algorithms based on collected data to show different possible outcomes.
- Agriculture
The agricultural industry is using machine learning for keeping track of watering schedules depending on the soil and the crop. Farmers are also using artificial intelligence to manage humidity, temperature, pesticides, weed growth, etc. Machine learning is also used in developing a probability model for predicting how each species will perform in a different environment.
- Manufacturing
Manufacturing industries employ the use of machine learning by keeping track of their machinery and performance indicators to manage production schedules. Automating report generation is a common method used in manufacturing businesses where production orders are updated automatically for managers and team leads to view. With supervision automated and reports generated, each department can easily keep track of quality assurance so all key performance indicators are met on time.
- Transportation
The transportation industry is another area where machine learning applications are used. Automotive machinery and assembly processes are deeply complicated which is why image recognition and risk assessment tools are used to optimize the manufacturing process. Beyond the manufacturing process, transportation industries also use prescriptive analytics to predict and avoid any risks like natural disasters along their route. You can also gauge and see how safe the travel routes are. Machine learning algorithms also use data to eliminate chances of stoppage.
How does dotnet report improve data management?
Every company in any industry generates huge amounts of data which helps them in making decisions, generating forecasts, and managing their clientele. We believe that the most efficient way of employing the machine learning industry in your workflows is if you stop doing everything yourself and let artificial intelligence take over. This is why our satisfied client base is using Dotnet report to generate their reports no matter how complex without writing a single code.
Creating actionable insights is easier now with our ad hoc reporting interface, customizable dashboard, and much more. Our clients are now using dotnet report to improve their business efficiency by not having to write complex codes every time they want to generate reports.
The future of business rests on the shoulders of the machine learning industry. The process of manually handling data has become outdated as it does not fully benefit the business processes. With machine learning and the increased use of tools like dotnet report, companies are able to better invest their resources to maximize their business efficiency. So, join those who know the real value of machine learning in pursuit of their business objectives and start your free trial or schedule a demo today.
