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 for this year. 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. Unsupervised Machine Learning
Does not require humans to intervene since the algorithms are designed to identify data groupings and patterns that are unseen. This type of learning is able to look at the data and identify the similarities.
This is ideal for companies that want to implement cross-selling plans. Unsupervised machine learning also uses a cluster analysis method, which mines data to find groupings. It uses K-means clustering and hierarchical clustering.
2. No-Code Machine Learning & Low-Code Machine Learning Development
No-code is becoming increasingly popular amongst companies. DataRobot, Clarifai, and Teachable Machines are all platforms that allow companies to function without requiring an engineer or developer.
These platforms allow the user to create their own tools with a drag-and drop interface, as opposed to requiring complicated coding to do so. A lot of money and time can be saved using these platforms by requiring less tech skills and less code writing. Too many business analysts don’t have the software coding and programming skill set that is needed, so in order to solve analytical problems, no-code and low-code applications are becoming increasingly necessary. Even machine learning engineers who have extensive experience can utilize and benefit from low-code applications in order to develop machine learning solutions.
3. 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.
4. Machine Learning Operationalization Management (MLOps)
This method focuses on the efficiency of machine learning models when they are in the deployment stage and maintenance stage. Data scientists and operations can come together to work as quickly as possible. This method helps solve the problem of weak communication.
5. Reinforcement Learning
This allows software to take the path of least resistance by having experience with an environment. This uses a reward and punishment system, and lets the machine learn by experimenting with a potential path and then deciding which one would have the best reward, thus allowing it to find solutions to issues efficiently.
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. Tiny ML
This method is quickly developing for AI and ML models that use machinery that is hardware-constrained, like microcontrollers or utility meters. The algorithms are designed to recognize simple commands from our voices or gestures.
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.