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Machine learning went quite a way from being a “wow effect” to more of a commodity. Now, as our demands towards technology grow and change, machine learning is adapting to them and surprising us with exciting new trends. While it’s impossible to make 100% accurate predictions, we still gave it a try and listed the most promising and anticipated ML trends for the upcoming 2022 with the help of our ML & Data Scientist specialist Alex Gedranovich.
No-code machine learning and AI
No-code ML is exactly what it sounds – it’s the process of building ML applications without the need to do excessive coding. Instead, you can use a drag-and-drop visual interface to assemble a machine learning application that would satisfy most of your requirements.
No-code ML comes from no-code software development. This concept is relatively new and was introduced as a way to shorten the development time and minimize the needed efforts. Instead of spending hours on manual code writing, users can use specialized programs and “construct” software applications instead of writing them from scratch. And while you can say that machine learning is too complex to be used in a drag-and-drop manner, this development method is already here and is becoming quite popular.
The main reasons behind the use of no-code ML are:
- Faster development and implementation: no-code ML development is much faster than traditional one;
- More affordable: if comparing no-code ML and traditional machine learning development, no-code development comes at a much lower cost;
- Simple and clear interface: no-code development does not require high technical skills from the user and offers a very intuitive and clear interface to work with.
Due to its simplicity, no-code machine learning will probably be used to create not so complex solutions. But if you need thorough analysis, deep customization, and control over the ML model, of course, it will be a better idea to go with the traditional approaches.
Tiny machine learning
Another revolutionary transformation in the field of machine learning is tinyML. Tiny Machine Learning was inspired by IoT and the main idea behind it is to enable ML-driven processes on IoT edge devices and devices with low power consumption. A good example of tinyML is a wake command that you give to your smartphone: either “Hey Siri” or “Hey Google”.
The reason behind tinyML is to make machine learning more versatile and to expand its usability. Several years ago, machine learning development required high computational power to handle the processes – but today, tinyML can be implemented to almost any device that has sufficient computing power.
In this way, machine learning becomes more affordable but it also brings us lower power consumption, lower latency, and lower required bandwidth while remaining secure. Same as no-code ML, tinyML is quite a niche solution but it certainly can benefit your company if you are dealing with IoT or embedded solutions.
Next in the list of machine learning trends is MLOps. As the name implies, MLOps was inspired by the DevOps methodology. If we address the definition, MLOps is a set of practices for transparent and seamless collaboration of data scientists (“development”) and operational specialists (“operations”).
Before the introduction of MLOps, machine learning development has always been associated with certain challenges, like scalability, development of proper ML pipelines, management of sensitive data at a scale, and communication between the teams. MLOps is aimed to resolve these issues by introducing standard practices to ML applications deployment.
While the phases of MLOps are pretty much the same as phases of traditional ML development, MLOps brings more transparency, eliminates communication gaps, and allows better scaling due to business objective-first design. You can say that with MLOps, you pay much more attention to the process of data collection and cleaning as well as to model training and validation. Hence, enterprises with an acute need for scalability will most definitely benefit from the deployment of the MLOps approach.
Generative adversarial networks (GANs)
GAN is kind of a buzzword these days but do you really know the meaning behind it? If not, let’s quickly explain what a generative adversarial network means.
A generative adversarial network is an architecture where two neural networks kind of compete with each other. One network (generative) generates images while the other, the discriminative network, tries to evaluate them. In this way, GANs do not require human control and are instead busy with self-education.
So what’s the reason behind the growing GANs popularity and its inclusion in the list of the biggest machine learning trends? Since GANs are capable of generating photorealistic images, it is used to create images for industrial design, computer games, interior design, etc and etc. As well, GAN systems help create 3D models of needed objects and improve the quality of available images.
Unsupervised machine learning
Another big thing that we will see in 2022 machine learning trends is unsupervised learning. Again, it’s easy to guess its meaning by its name: unsupervised learning means there is no human intervention in the machine learning process. Instead, the ML model receives unlabelled data and is free to draw any conclusions based on it.
The biggest difference between supervised learning and unsupervised learning is the data. With supervised learning, the data is labeled, meaning people already prepared it for the ML model. With unsupervised learning, the data is unlabelled, meaning it does not have any labels and is not separated into groups or categories. So why would you allow an ML model to be so independent about working with data?
The thing is, when the data is unlabelled, the ML model is free to determine any possible insights, dependencies, and relationships as it “sees it”. Such an approach often provides surprisingly efficient results and works especially well for anomaly detection, computer vision, and medical imaging, defining customer personas, and categorizing content on the website.
When talking about the learning process of an ML model, it is a well-known fact that the more data the model gets as an input, the better the results are. However, in some cases it is too complex or inefficient to use hundreds of images to teach the model to recognize a certain object. This is where one-shot learning steps in.
One-shot learning advocates learning with one image only. You read that right – you will need only one image to teach a model about a certain object. Here is how it works.
One-shot learning uses a Siamese network to teach a model. A Siamese network consists of two subnetworks that are a mirror image of each other. These subnetworks “compare” a reference image, stored in the database, with an image that needs to be identified. The output is a similarity score where the system defines how much a new, single image is similar to the reference one. In this way, in order for the system to identify a new image, it simply has to compare a new and an old one and make a decision based on the level of similarity between the two.
This approach is widely used in facial recognition and other use cases that require the use of a small number of images (e.g. the photos of all employees in the company). As well, there are also such methods as few-shot learning and even zero-shot learning and all of them share one big benefit: you don’t need too much data to efficiently train a model.
As you can see from these machine learning trends, this technology is slowly shifting towards automation, speed, and efficiency as well as becoming more specialized. Since machine learning is a valuable asset for any company, it has to become more affordable and versatile in order to benefit businesses of different sizes and types. So it’s safe to assume that in the future, we might see even more exciting applications of machine learning and new approaches to ML models development.