- No-code machine learning
Although most machine learning is set up and handled by computer code, it is no longer the only option. Rather, no-code machine learning, a way of programming machine learning applications without necessarily passing through the long, cumbersome, complicated processes of pre-processing, designing, modelling, design, collection of new data, retraining and deployment. No-code machine learning has some advantages, which include quick implementation, reduced costs and simplicity. Because of its potential to simplify the machine-learning process significantly, it reduces the time that one needs to become an ML expert, making ML applications accessible to developers. This is one of the areas that is fast gaining momentum in the ML landscape.
- TinyML
In a world where IoT solutions are increasingly being adopted, TinyML is a good addition. Although there are large machine learning applications, their usability is limited. This makes it necessary to have smaller applications. By running small-scale machine learning programs on IoT and edge devices, lower latency, low power consumption and lower bandwidth are achieved. This is particularly good for IoT devices because data does not need to be sent out to data processing centers. This trend is opening up applications in sectors like predictive maintenance, healthcare, and agriculture, where IoT devices are making a difference.
- AutoML
AutoML aims at making the building of ML applications accessible to developers. Since machine learning has become widely adopted in many industries, there has been an increase in demand for off-shelf solutions. AutoML seeks to bridge the gap by making simple solutions accessible to everyone without relying on ML experts. This simplifies the work of data scientists who have to focus on aspects like preprocessing, features development, modelling and development of neural networks. These are tasks that are complex and require the input of professionals. Since some of the tasks can be complex, AutoML simplifies things through templates. AutoML enhances data labelling through tools and automates the tuning of neural network architectures.
- Full-stack deep learning
There has been a growing demand for full-stack deep learning in creating libraries and frameworks to help in the automation of tasks. This has led to a rise in new business needs. Full-stack deep learning entails developing deep learning models and connecting them to the external world for users to use them. Engineers have to wrap the deep learning models to infrastructure.
- Unsupervised ML
Automation is a growing trend in many areas, and data science solutions are also taking this into consideration. This means that human intervention in various areas will be minimized significantly. However, machines cannot learn in a vacuum. Instead, they have to take new information from humans and analyze them. Meaning they need human data scientists to feed the information. However, the unsupervised ML focuses on unlabeled data where it has to draw conclusions on its own without guidance from data scientists. With these capabilities, ML can be useful in studying data structures quickly and identifying patterns. The information is then used to enhance and automate decision-making. Clustering is one of the techniques that can be used to investigate data.
