Although machine learning is one part of the greater AI market, it is the most commonly implemented form of AI and growing rapidly in business. The machine learning market reached a value of about $1.41 billion in 2020 and is expected to reach $8.81 billion by 2025, according to 360 Research Reports.
Machine learning makes our lives easier. When properly trained, they can complete tasks more efficiently than a human. Understanding the possibilities and recent innovations of ML technology is important for businesses so that they can plot a course for the most efficient ways of conducting their business. It is also important to stay up to date to maintain competitiveness in the industry.
Machine learning models have come a long way before being adopted into production. Here are the 9 Machine learning trends can benefit your business in 2022.
It can take time for a web request to send data to a large server for it to be processed by a machine learning algorithm and then sent back.
Instead, a more desirable approach might be to use ML programs on edge devices – we can achieve lower latency, lower power consumption, lower required bandwidth, and ensure user privacy.
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.
Auto-ML brings improved data labeling tools to the table and enables the possibility of automatic tuning of neural network architectures. Evgeniy Krasnokutsky PhD, AI/ML Solution Architect at MobiDev, explains: “Traditionally, data labeling has been done manually by outsourced labor.
This brings in a great deal of risk due to human error. Since AutoML aptly automates much of the labeling process, the risk of human error is much lower.”
AutoML system allows you to provide the labeled training data as input and receive an optimized model as output. There are several ways of going about this. One approach is for the software to simply train every kind of model on the data and pick the one that works best. A refinement of this would be for it to build one or more ensemble models that combine the other models, which sometimes (but not always) gives better results.
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:
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.
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.
Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.
GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a 6. zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.
A large demand for “full-stack deep learning” results in the creation of libraries and frameworks that help engineers to automate some shipment tasks and education courses that help engineers to quickly adapt to new business needs.
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?
In machine learning, there are three paradigms: supervised learning, unsupervised learning, and reinforcement learning. In reinforcement learning, the machine learning system learns from direct experiences with its environment. The environment can use a reward/punishment system to assign value to the observations that the ML system sees. Ultimately, the system will want to achieve the highest level of reward or value, similar to positive reinforcement training for animals.
This has a great deal of application in video game and board game AI. However, when safety is a critical feature of the application, reinforcement ML may not be the best idea. Since the algorithm comes to conclusions with random actions, it may deliberately make unsafe decisions in the process of learning. This can endanger users if left unchecked. There are safer reinforcement learning systems in development to help with this issue that take safety into account for their algorithms.
Data collection is essential for machine learning practices. However, it is also one of the most tedious tasks and can be subject to error if done incorrectly. The performance of the machine learning algorithm heavily depends on the quality and type of data that is provided. A model trained to recognize various breeds of domestic dogs would need new classifier training to recognize and categorize wild wolves.
Few shot learning focuses on limited data. While this has limitations, it does have various applications in fields like image classification, facial recognition, and text classification. Although not requiring a great deal of data to produce a usable model is helpful, it cannot be used for extremely complex solutions.
Likewise, one shot learning uses even less data. However, it has some useful applications for facial recognition. For example, one could compare a provided passport ID photo to the image of a person through a camera. This only requires data that is already present and does not need a large database of information.
Zero shot learning is an initially confusing prospect. How can machine learning algorithms function without initial data? Zero shot ML systems observe a subject and use information about that object to predict what classification they may fall into. This is possible for humans. For example, a human who had never seen a tiger before but had seen a housecat would probably be able to identify the tiger as some kind of feline animal. Source
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