What is machine learning? Machine Learning (ML) is one of the most exciting areas in the field of artificial intelligence (AI). The advancements in ML are helping businesses across industries to make better use of their data and improve their decision-making capabilities.
Top Machine Learning Trends Of 2022
As more companies adopt machine learning to gain a competitive advantage, you can expect more innovations and developments in this space. In this article, discussed are some key trends that will shape machine learning in 2022:
Enhancement of ML-models with AI
In addition to being utilized for training and validation of ML models, which is still the most popular use case today, you can expect AI to have a broad range of applications in the future. One example would be using deep learning techniques for image synthesis (for example, via generative adversarial networks).
Another could be using reinforcement learning (RL) techniques as a part of an autonomous driving system that learns how to drive by observing human drivers on the road and taking actions based on their behavior.
Machine Learning expertise and talent scarcity
As the demand for machine learning talent continues to grow, there will be a need for more experts in the field. Today, there are more than 1 million unfilled jobs in the US alone and many companies need help to hire enough people with machine learning expertise.
This situation won’t get any better soon—the problem is expected to get worse before it gets better because there are so few people with a deep understanding of how neural networks work and how they can be applied successfully in different industries.
As per Micro Focus solution experts, “Machine learning applications learn from the input data and continuously improve the accuracy of outputs using automated optimization methods.”
Intensification of Linux for machine learning applications
Linux is a key component of machine learning. Linux is the most popular machine-learning operating system and can run on any hardware platform.
Linux is used in all phases of machine learning development, including training, model deployment and serving models. Since Linux has been tested to meet strict requirements for security, performance and scalability, it’s well suited for deployment at scale with containers.
Increased security concerns for machine learning
Machine learning is a powerful tool, but it can also be used for nefarious purposes. The biggest consequence of this is the potential for privacy violations: if someone has access to your personal data, they could use machine learning to identify you and glean information about your life that you would rather keep private.
This could come from any number of sources— like a retail website that collects credit card data or a dating app that tracks user preference.
Also, so far, there’s no clear way to determine if a given app or site uses tools that gather too much information about its users.
Application of machine learning across industries
Machine learning is being used across industries. From healthcare to retail, transportation, manufacturing and more, many organizations are seeing the value that machine learning can bring to their business. And this is not just because of the recent proliferation of cloud computing and the internet of things (IoT).
The use cases for machine learning have grown exponentially over the last few years as businesses have realized that it can optimize their supply chain management processes.
With the advent of machine learning, it is possible to achieve a lot more in a shorter span of time. You are already witnessing the increased adoption of this technology by industry leaders across various verticals and will see many more innovations in the coming years.