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Top 5 Machine Learning Trends For 2021

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  • Difficulty Level : Easy
  • Last Updated : 19 Jan, 2021
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One technology that has become more and more popular with time is Machine Learning! These days, chances are that you have heard of the popularity of Machine Learning and Artificial Intelligence if you are in any way connected to the tech industry (And sometimes even if you are not!) Machine Learning is incorporated in more and more companies like Google (which is expected) or even Netflix (Wow!) and even smaller companies that use ML algorithms to obtain insights from the data. Market research even predicts that the global machine learning market which is currently around $7.3 Billion in 2020 will expand to $30.6 Billion in 2024. This is great news for Machine Learning and also shows an upward trend in 2021 for this technology.


There are many new innovations and Machine Learning Trends that may come to the forefront in 2021. Already, there are many applications of Machine Learning in the industry such as its integration with the Internet of Things, more prevalent use in industries such as cybersecurity, Finance, Medicine, etc. According to a Salesforce Research study, 83% of IT leaders believe that Machine Learning and Artificial Intelligence is changing the customer engagement experience for the better. This clearly shows that ML as a technology is only increasing in popularity.

So as we reach the end of 2020 and the beginning of 2021, let’s see some of the key Machine Learning trends that may change the future for the better. These trends not only showcase the integration of ML in various industries, but also the improvements in the technology itself.

1. The Intersection Of ML and IoT

IoT is already an established technology wherein multiple devices or “things” are connected across a network and they can communicate with each other. These devices are increasing continually, so much so that there might be more than 64 billion IoT devices by 2025. All these devices collect data that can be analyzed and studied to obtain useful insights. That’s where Machine Learning becomes so important! Machine Learning algorithms can be used to convert the data collected by IoT devices into useful actionable results.

An example of this is Green Horizons, a project created by IBM’s China Research Lab that aims to control the pollution levels to more breathable standards. This can be done using an IoT network where sensors collect emissions from vehicles, pollen levels, airflow direction, weather, traffic levels, etc, and then ML algorithms are used to find the best way to reduce these emissions. The intersection of ML and IoT can also be seen in the field of Smart vehicles where self-driving cars need to be extremely accurate and all the parts need to communicate with each other in milliseconds on the road. This shows how important the combination of these technologies is. Gartner even predicts that more than 80% of enterprise IoT projects will use Artificial Intelligence and Machine Learning in some form by 2022, which is much higher than the 10% of projects using it currently.

2. Automated Machine Learning

The next stage of development in Machine Learning is Automated Machine Learning! It’s a godsend for people who are not experts in the complicated world of Machine Learning and also for experienced data scientists and analysts. Automated Machine Learning allows these data scientists to create Machine Learning models with higher efficiency and productivity while having top-notch quality.

So a tool like AutoML can be used to train high-quality custom machine learning models for classification, regression, and clustering without much knowledge of programming. It can easily deliver the right amount of customization without a detailed understanding of the complex workflow of Machine Learning. It can also help in using machine learning best practices while saving time and resources. One such example of AutoML is Automated machine learning provided by Microsoft Azure that you can use to build and deploy predictive models.

3. Machine Learning in Cyber Security

As Machine Learning becomes more and more popular in current times, it is also increasing its applications in many different industries. One of the most popular among them is the Cyber Security industry. Machine Learning has many applications in Cyber Security including improving available antivirus software, fighting cyber-crime that also uses Machine Learning capabilities, identifying cyber threats, and so on.

Machine Learning is also used to create Smart Antivirus software that can identify any virus or malware by its abnormal behavior rather than just using its signature like normal Antivirus. So the smart Antivirus can identify older threats from previously encountered viruses and also new threats from viruses that were recently created by analyzing their auspicious behavior. Since these days many companies are integrating Machine Learning in Cybersecurity, the most common examples of this are Chronicle, a cybersecurity company handled by Alphabet (Google’s parent company), Sqrrl, a company founded by ex-National Security Agency employees (Scary!), etc.

4. Rise of AI Ethics

Now that Artificial Intelligence and Machine Learning is on the rise, it is equally important to discuss the ethics of these technologies. It is very easy to create tech that is intelligent and has independent thinking capabilities but what happens after? What if a self-driving car kills a person? What if a Machine Learning algorithm is biased towards certain people because the data is biased? Should robots have any rights under the law or not? What will happen to people’s jobs if Machine Learning becomes more widespread? These are all Ethics questions that are important and need to be discussed more in 2021.

There have already been many controversies relating to AI Ethics. The most popular one was when Amazon found out that their Machine Learning based recruiting algorithm was biased against women in 2018. This is because it was trained on data where most of the candidates were men, so the algorithm also favored men over women. Another much recent scandal was in 2020 only when Google fired Dr. Timnit Gebru, a prominent researcher on racial bias in technology who was studying the bias in Google’s Artificial Intelligence systems.

5. AI Engineering

Everyone has heard about software engineering, but now it’s AI Engineering that on the rise as a profession! This is a very important development because the integration of Artificial Intelligence and Machine Learning in the industry has been very ad-hoc and haphazard without any regulations of best practices. That’s why Gartner even predicts that only 53% of AI and ML projects complete the journey from a prototype to full production in a company while the rest 47% face failure.

That’s where AI Engineering comes in! A streamlined AI Engineering strategy for a company provides great performance, reliability, and scalability from a machine learning algorithm which ensures a return on the investment in AI. This includes a heavy focus on DataOps, ModelOps, DevOps, etc. with the artificial intelligence projects being a part of the overall DevOps strategy in a company and not just an ad-hoc practice in some projects.


With the beginning of the new year, there will be many new trends observed in Machine Learning 2021. Of course, 2020 was a year that no one could have expected (and not in a good way!) so who knows what 2021 truly holds? But it’s obvious that some trends in Machine Learning are here to stay such as its integration into many new fields such as cybersecurity, Finance, and even Healthcare. More and more companies are adopting Machine Learning so that this technology is no longer exclusive to just tech giants like Google, Facebook, Microsoft, etc. This also means that AI Ethics is becoming necessary with many companies also having an Ethics board. So let’s see where the developments in Machine Learning go in 2021 and well into the future!

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