With 20% of C-level executives reporting that they are using machine learning as a core part of their business, it comes as no surprise that the value of the global machine learning market is likely to reach $117 billion by the end of 2027.
We’ve scoured the web to round up the seven most talked about machine learning trends. These are our predictions for what is going to be the most popular way of developing technology in the foreseeable future and what you need to know right now.
1. Sustainable Technology Growth
Sustainable and environmentally friendly technology refers to produced or services rendered with an awareness of the ethical and environmental costs associated with their creation, usage, and disposal. Organizations will continue to look for ways of shifting practices to create sustainable products that reduce a company’s carbon footprint by minimizing environmental and ecological risks.
2. Increased AI and Machine Learning
The benefits of AI and ML are becoming more mainstream and organizations are seeking talent with the skills to implement these technologies. According to this recent study organizations expect their investment in AI-related talent to increase by 50-100 percent over the next three years.
3. Tiny ML
According to global tech market advisory firm, ABI Research, a total of 2.5 billion devices are expected to be shipped with a Tiny Machine Learning (TinyML) chipset in 2030. Over the course of your day, you use Tiny ML more than you realize. Tasks like scrolling through your phone, snapping a selfie, checking your email, all use machine learning models.
This method is quickly developing for AI and ML models that use machinery that is hardware-constrained called microcontrollers to perform automated tasks. The algorithms are designed to recognize simple commands from our voices or gestures.
4. Automated Machine Learning (AutoML)
Automate the traditional manual process, like data labeling. Anyone can have access to AutoML, and it also has the added benefit of reducing human error. Just about every stage is automated in this process. This is great because we’re no longer spending too much time analyzing and modeling data. Semi- and self-supervised learning will help with the need for labeling data without continuing to spend money on human annotators since manually labeled data will be minimized.
5. AI Risk and Security Management
Leaders hoping to shift to better understand the types of risks they are taking on and their underlying causes. If something goes wrong along the process – from data, technology and security, the frontline needs to understand if it is human judgment or scripting errors that can compromise privacy, compromise, and security.
6. Robotic Process Automation (RPA)
RPA lets a system automate any process that can be repetitive, allowing the user to spend their time working on other projects that require more critical human thinking skills. But the thing has to be pre-defined before the RPA bot can process it. Minimum deviation will cause an RPA bot to fail. Machine learning put in the RPA can help, which gives more fluidity to making acceptable changes in the process.
7. Hyper Automation
Another emerging trend is hyper automation, which is a time-saving way to improve customer service and speed up various processes. Advanced technologies that help to power hyper automation, including Machine Learning, Artificial Intelligence (AI), cognitive process automation, and more. Gartner has identified Hyper automation as one of the top 10 strategic technology trends.
Legacy infrastructure and processes can slow an organization down and affect their ability to be competitive. Simple, task-based automation does not deliver the cross-functional results that will drive business decision making and results. Aside from improving the customer service experience, hyper automation can also help accomplish other important tasks at a faster rate, such as system integration and organization, as well as improving worker productivity.
Make Machine Learning Part of Your World
Getting in the know now is important in order to stay on top of relevance and your career. By knowing what is coming around the corner, you can guarantee that your work and career will always be in demand.
Interested in exploring the world of machine learning and AI? Get started with the Intro to Machine Learning with PyTorch and Intro to Machine Learning with TensorFlow Nanodegrees within our School of Artificial Intelligence. Already familiar with the machine learning basics and want to step up your skills? Check out the Machine Learning Engineer for Microsoft Azure and the Machine Learning DevOps Engineer Nanodegrees.