Machine learning is a field that has been around for decades. In the past few years, it’s gone from being a niche interest to one of the most sought-after skills in tech and business, as more and more people realize its potential. There are many predictions about what will happen with machine learning over the next five years – here are five of them.




Global Trends in Machine Learning in 2022

1. Machine learning and IoT

This is the most anticipated trend among tech experts.5G will have a major impact on both these emerging technologies. As the adoption of 5G increases, IOT will serve as the enabler for the advancement of Machine Learning. Due to the fast speeds of 5G and the spread of IOT devices the volume of data will increase exponentially, leading to the demand for sophisticated Machine Learning practices and solutions. IoT devices allow several digital devices to connect via the internet to form a network. As the number of linked devices grows, the volume of data sent grows as well. Many industries, including the environment, healthcare, education, and IT, will benefit from the adoption of IoT devices. This combination will also ensure that there are fewer internet failures and data leakage.

2. Automated Machine Learning

Automated machine learning will assist professionals in the development of efficient models (algorithms) and solutions that will lead to increased productivity and new and innovative business solutions. Future innovations will be focused on synthesizing and analysing vast amounts of data to develop optimum solutions and help solve complex problems in any industry such as logistics, customer journeys and experience, healthcare and science and technology. Automated Machine Learning (AutoML) will increase the efficiency and effectiveness of work without the need for extensive programming skills or experience. The greatest challenge Machine Learning faces today is the time taken to perfect models and algorithms to achieve the desired output. The major benefits of Automated Machine Learning are:

Efficiency – It speeds up and simplifies the machine learning process and reduces training time of machine learning models.

Cost savings — Having a faster, more efficient machine learning process means a company can save money by devoting less of its budget to maintaining that process.

3. Improved Cybersecurity

Most of our appliances and software have evolved into smart devices, demonstrating a high level of technological advancement. Since they are always connected to the internet, there is a pressing need to boost the level of security of these devices. The major digital device and technology providers such as Google, Apple Samsung are constantly improving and making major investments to ensure that the privacy and data protection requirements of their customers are met. By utilizing machine learning, companies and professionals in cyber security may develop cutting-edge anti-virus models that can deter cybercrime, hackers, and attack minimization by assisting the model in identifying different types of threats, such as malware behaviour, code differences, and new infections.

4. Improved Artificial Intelligence

With the advancement of artificial intelligence and machine learning, it is necessary to improve data quality and guidelines for these technologies. With advancements in technology comes the commensurate need for the improvement in data quality and Machine Learning algorithms; otherwise, machines will be unable to behave in the way we want them too, as is now the case with self-driving cars. One of the most common causes of self-driving car failures is the inability of artificial intelligence to function as expected. There are two main reasons for this. To begin with, solution designers and developers could be using data that contains bias. i.e., favouring one side over the other,

In addition, a lack of data normalization might cause machine learning algorithms to learn from incorrect types of data. This has the potential to introduce bias into the neural network of the machine.

Advances in Machine Learning will lead to improvements in Artificial Intelligence, thereby making self-driving cars a reality soon.

5. Unsupervised Machine Learning

As automation develops, more data science solutions that do not require human intervention are required. Unsupervised machine learning is a trend that has shown potential in a variety of sectors and applications. We already know that machines can’t learn in a vacuum thanks to earlier efforts. They must be able to take fresh information and analyse it to develop a solution or get the desired outcome. However, this usually necessitates the use of human data scientists to enter the data into the system.

Unsupervised machine learning focuses on data that hasn’t been labelled. Unsupervised machine learning programs must form their own conclusions without the help of a data scientist. This can be used to swiftly examine data structures in order to uncover potentially beneficial patterns, and then use that information to improve and automate decision-making. Clustering is a technique that can be used to analyze data. Machine learning programs using Clustering can better grasp data sets and trends by grouping data points with shared properties.

Machine Learning applications in the Real World

Listed below are a few companies that are utilizing machine learning in novel and innovative ways today.

1. Yelp

Yelp publishes crowd-sourced reviews about businesses. It also operates Yelp Reservations, a table reservation service. Before we visit new restaurants most of us tend to check out the reviews of the business before committing to giving it a try. Our mobile phones are used to take pictures (of the food and ambience) to validate the experience This is one of the main reasons Yelp has become so popular. While Yelp may not appear to be a tech company at first look, machine learning is being used to improve the user experience. Pictures are almost as important to Yelp as user ratings, it’s no wonder that the company is constantly working to improve its image processing capabilities. When it originally introduced its picture classification system a few years back, Yelp turned to machine learning. Yelp’s machine learning algorithms assist the company’s human personnel in more efficiently compiling, categorizing, and labelling images — no minor accomplishment when dealing with tens of millions of images.

2. Pinterest

Pinterest occupies an odd role in the social media ecosystem, whether you’re a die-hard pinner or have never used it before. Given that Pinterest’s principal job is to curate existing content, it stands to reason that investing in technologies that can make this process more efficient would be a top priority — and it is. Pinterest bought Kosei, a machine learning company that focused on commercial machine learning applications, in 2015. (specifically, content discovery and recommendation algorithms). Machine learning is now used in practically every element of Pinterest’s business, from spam detection to ad monetization and newsletter subscriber retention.

3. Facebook

The Messenger service on Facebook is one of the most fascinating features of the world’s largest social media network. Messenger has become a sort of chatbot testing ground. Any developer can construct a chatbot and submit it to Facebook Messenger. So even a small firm with limited engineering resources may use chatbots to improve customer service and retention. Of course, Facebook is interested in other applications of machine learning. Facebook is investigating computer vision algorithms that can “read” photographs to visually challenged people.

Machine Learning Training

The potential for machine learning is growing with each passing day and you can’t let precious opportunities pass you by. You can learn these skills through EZY Skills, an online training academy that offers eLearning courses in emerging technologies such as Machine Learning.

EZY Skills has a Certified Machine Learning Specialist eLearning course that is comprised of three courses that develop skills in Machine Learning practices, models and algorithms, as well as Machine Learning systems that can perform a range of data analysis processing tasks.