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Machine learning solutions in 2023

15 December 2022
Machine learning solutions-s

Machine learning solutions are more diverse and accessible going into 2023 than ever before.

Developments in ML and AI over the past year have brought this technology to the mainstream. Businesses and users should be informed about a few machine learning trends, including the markets where it could boom in 2023.

Types of machine learning

There are thousands of unique machine learning models today, but they all fall into a few common categories. The differences between the various types of machine learning stem primarily from how the algorithms are trained.

Supervised learning

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Supervised learning

The first type of machine learning model is supervised learning. Gartner predicted correctly that supervised learning would remain the most popular type of ML algorithm through 2022.

This ML model algorithm is trained using known and labeled input and output data. The idea is to teach it to understand and recognize connections between certain types. It is given a set of input-output pairs, processes them and refines its logic to be more accurate to the desired result.

Supervised learning

Supervised learning is usually used for algorithms designed to recognize or categorize data or make predictions based on input.

Unsupervised learning

Unsupervised learning takes the opposite approach to training machine learning algorithms. In this type of model, training data is not labeled. There are no preassigned input-output pairings.

Unsupervised learning is most often used for clustering or classification by association. It is also popular with deep learning models, which tend to handle unstructured data better than other types of machine learning.

For example, an algorithm might be given many unlabeled photos of dogs and cats and trained to recognize two different types of animals. Eventually, the algorithm would identify common traits in pictures of dogs and the differences between those and those of cats. It could cluster future photos into either the “dog” or “cat” groups.

In unsupervised learning, the algorithm is trusted to recognize similarities between data points. This can be helpful in cases where developers don’t know what the connections between points in a training data set are going in. For instance, they might have a wealth of customer information but need help understanding certain patterns or trends.

Semi-supervised learning

As the name suggests, semi-supervised learning utilizes aspects of both supervised and unsupervised learning models. Labeled and unlabeled data is used in the training process. Labeled information is often used first to get the algorithm on the right track and unlabeled data is utilized later.

This model is a good choice in cases with some labeled or known data available but not enough for fully supervised learning. Similarly, information might be available, but developers might need more time or resources to label all of it manually.

Semi-supervised learning

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The lack of labeled training data doesn’t take away from the effectiveness of semi-supervised learning models. In fact, some experts say they are often more accurate than fully supervised versions due to the inclusion of unlabeled data.

Reinforcement learning

Reinforcement learning takes a unique approach to training machine learning algorithms. It is less popular than the above three types of machine learning models, largely because it requires significant computing power.

Reinforcement learning uses positive and negative feedback to teach an algorithm to make predictions correctly. For example, it might analyze chess games and try to predict whether a move will win or lose in certain situations. Correct predictions get positive feedback, while incorrect ones get negative feedback.

Machine learning trends

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As of 2022, reinforcement learning is still a niche machine learning model. Its main uses today are in scenarios that can be fully simulated, such as video game development or training autonomous vehicles.

Top machine learning trends 2023

Some interesting trends in AI and machine learning are appearing for 2023. Some of these are the result of technologies finally coming of age, while others are due to certain machine learning applications growing in demand.

Generative AI

Generative AI will be one of the top trends in machine learning in 2023.. This machine learning technology has been gaining popularity online in 2022, thanks to the humorous artwork users can generate with AI.

Generative AI
For example, innovative video game developers are using machine learning models to generate graphics for VR games. The metaverse is sparking more interest in virtual reality among consumers, but the tech is still taking some time to catch up with expectations. One of the industry’s biggest challenges is the computing power and memory needed to create and store 360-degree 3D worlds.

Generative machine learning is perfect for addressing this challenge. Developers are using generative AI to create randomized, unique worlds in real-time, which could drastically reduce the development time for new video games. It’s also making artwork much more accessible, something that is highly valuable for all sorts of business applications.

For example, authors and independent publishers can generate original cover artwork using AI. Musicians can fill in the gaps in new tracks using AI-generated music clips. Businesses can create their own stock photos using AI portrait generators rather than hiring models.

Transparent machine learning

There has been a lot of media attention over recent years surrounding data bias in artificial intelligence and machine learning. Developers have been working hard to address this issue, resulting in a new type of algorithm that will be popular in 2023 and beyond: transparent machine learning.

This type of artificial intelligence, also known as explainable AI, is designed to be more user-friendly and easier to understand. The main issue with data bias is that developers can’t see how AI is making decisions. Prejudices can often go undetected until AI has been used for a long time and patterns crop up.

This conventional type of artificial intelligence and machine learning is called black-box AI. Explainable AI uses innovative development techniques to allow developers to see how it reaches certain conclusions.

Transparent machine learning

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These new algorithms, sometimes called “white box AI,” are much more complicated to build and train. However, they are likely the future of the industry. Developers that can watch a machine learning algorithm form new connections and conclusions can actively catch and remove biases. This could lead to machine learning models fulfilling the dream of truly objective AI.

No-code and low-code machine learning

No-code and low-code ML platforms could be one of the top AI and machine learning trends in 2023. AI and ML are no longer niche technologies. Everyone wants to try out their own ML model today, although many people lack the coding know-how to develop an algorithm.

Custom machine learning solutions have begun popping up in the market designed to work without any coding by the user. These innovative platforms, known as no-code AI, use GUI-based coding. Users drag and drop pieces or select desired settings while the platform does the actual coding in the background. Low code is similar but does require a bit of coding.

No-code solutions

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No-code and low-code machine learning tools will open up the industry to a wave of new users. These platforms are making ML far more accessible and breaking down technical barriers that have prevented many people from adopting AI and ML. As a result, countless new machine learning applications are sure to appear over the next year.

Social and educational AI

AI and ML are increasingly impacting the classroom, from elementary school to postsecondary education. Part of this is due to the surging popularity of STEM education. Students are excited about and interested in AI, coding and technology today. Teachers are developing many new methods for exploring STEM in class and collaborating with students’ non-STEM courses.

Online education is equally important in the rising popularity of educational AI and ML applications. Nationwide surveys show that as of 2022, 55% of Americans agree that the quality of online learning is equal to or better than in-person education. The COVID-19 pandemic may have forced everyone to try online learning, but many people continue using it out of preference.

AI and machine learning are highly effective tools for augmenting the online learning experience for all ages. For example, the massively popular language learning platform Duolingo uses AI and ML to customize students’ learning experiences and speed.

ML platforms

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Duolingo used PyTorch deep learning on Amazon Web Services to build a platform that can translate dozens of languages and tell which areas a student needs more practice with. Platforms like this are likely to become even more popular as online learning sticks around for the long term.

AI and machine learning solutions are also helping younger students. The AI-powered learning and socialization robot Moxie has made headlines over the past couple of years due to its intelligence and fun design. The robot remembers children’s names and faces, recalls conversations with them, and can play games.

Combining AI and IoT

One of the top machine learning trends going into 2023 is the collision of AI and IoT technologies. IoT has become particularly popular in industrial sectors, such as manufacturing, over the past year. At the same time, AI is revolutionizing how people and businesses work.

IOT sensors

A perfect example of this is predictive or preventive maintenance, which is gaining popularity in manufacturing and construction. IoT sensors monitor equipment performance, such as a construction vehicle. That data is sent back to a hub where an AI algorithm analyzes it. If the algorithm detects abnormal performance statistics, it will alert maintenance personnel that the item needs a checkup.

AI-IoT collaborations allow businesses to save money, reduce waste and operate more efficiently. There will likely be a surge in the applications for AI-IoT tools in 2023. Other popular examples include warehouse robotics and supply chain automation.

Top machine learning solutions for 2023

There are all kinds of great AI and ML solutions available today, from automated machine learning to code-free machine learning systems. Customers and developers new to ML may want input from machine learning experts who can provide guidance on specific circumstances.

It also helps to know a bit about the playing field. A few predictive analytics and machine learning solutions stand out among the best platforms going into 2023. Plus, there are also a couple of lesser-known options worth trying out.

Amazon SageMaker

Amazon Web Services (AWS) is arguably the most popular machine learning solutions development company today. SageMaker has long been a favorite platform for business machine learning tools. It’s a great place to start for customers shopping for a good machine learning platform in 2023.

Some key features of AWS SageMaker are continuous integration, autoscaling and a wealth of security features. Customers noted in their reviews that the AWS pricing model for SageMaker is highly affordable, making it a great value option. SageMaker is best suited for businesses, developers, machine learning solutions architects and data scientists.

Microsoft Azure Machine Learning Studio

Microsoft’s Azure Machine Learning Studio is a GUI-based low-code platform that’s great for various applications. Users’ favorite features include compatibility with IoT infrastructure, end-to-end support from Microsoft, speedy development and virtually no coding.

Microsoft Azure is unique in the machine learning market since it is low-code but capable of handling large-scale enterprise applications. This makes it a great fit for businesses with ML uses in mind but not necessarily the programming personnel to code custom algorithms.

Solutions overview

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Google AutoML

Google has long been a leading machine learning solutions company. Its cloud-based AutoML platform does not disappoint. It’s the perfect method for diving into machine learning without spending months learning to code.

AutoML is a no-code, GUI-based machine learning development platform that is particularly popular among small businesses. Google does not offer a free plan to use the program, but it provides custom prices, so companies can expect to get a fair rate.

One fan-favorite feature of AutoML is the pretrained models that are included. They are ideal for those who need a popular ML model without a lengthy development process. Google is also known to offer plenty of technical support for users.

Apple CreateML

One of the lesser-known but highly effective machine learning apps to try out in 2023 is Apple’s CreateML. This platform is perfect for Mac users, especially those with minimal or no coding knowledge.

Apple’s no-code machine learning tool allows users to train algorithms in various content types, such as photos, text or videos. CreateML is optimized for fast performance, specifically on Mac devices, so there’s no need to worry about having enough computing power on any of Apple’s computers.


Alteryx has been gaining ground in machine learning communities recently, and for good reason. This unique platform could be a big hit in 2023 due to its highly approachable, flexible design. Alteryx is most popular for data analysts.

What makes Alteryx stand out among other machine learning solutions is that it allows for code as well as no-code development. This is perfect for users who are new to machine learning but want to learn to code eventually. Similarly, it is great for businesses with machine learning teams consisting of programmers and those without coding knowledge.

The year ahead for machine learning

The machine learning and artificial intelligence industry have grown exponentially over recent years. Innovations and trends hint at another big year ahead in 2023. This is a great time for businesses and independent developers to adopt AI and ML, thanks to a wealth of highly accessible new development platforms.

Author bio

April Miller is a senior writer with more than 3 years of experience writing on AI and ML topics.

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