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Top 8 Machine Learning Trends for 2023

Top 8 Machine Learning Trends for 2023

Machine learning is one of the widely adopted technology in 2021. And it is going to be the same for 2023. In this article, I’ve compiled a list of machine learning trends that will continue to set the market on fire in the years to come.

With the advent of AI in all spheres of life, there has been a massive growth in the use of machine learning technologies. The next few years is also expected to bring in more new technology for better analysis and data interpretation. As the demand for such solutions is increasing with each passing day, it is becoming crucial to focus on what the future holds for ML and AI. 

But machine learning is a complex subject, with new trends, techniques, and tools appearing rapidly. This can make it challenging for organizations to keep up with the latest developments. 

To help solve this problem, I’ve put together this list of the top 8 machine learning trends for 2023. These are the key trends that I believe will impact the majority of organizations who use machine learning in the next few years.

Top 8 ML Trends for 2022

Today, businesses are becoming smarter and more successful with data science and machine learning. Big tech giants like Facebook, Amazon, Google, Microsoft, and many more are successful only because they rely on AI, machine learning, and data science. 

By incorporating machine learning for business, your company can collect valuable insights, analyze them, and formulate competitive and innovative business strategies. Strategies derived from data analysis led to higher customer satisfaction and experience.  

Here are top 8 machine learning trends helping businesses grow by incorporating machine learning in their daily operations. 

1) Low-code or no-code development 

The number of machine learning projects and the demand for data scientists are expected to increase in the coming years. Although this is good news, it will also create a problem in terms of sourcing talent. Low-code/no-code ML platforms have already started to emerge, but they won't be mainstream until 2023. 

Low code and no-code development tools are suitable for users that don't have coding skills. The low-code/no-code platforms allow users to create programs by dragging and dropping items or without manual coding. This can make ML open to business users, in addition to data scientists, enabling the model deployment and application into the company's ecosystem. Low code development tools also offer API integrations, and AI/ML facilities for businesses to create innovative and productive applications faster.

2) Enhanced user experience with data

The next trend on our list is the use of machine learning in enhancing user experience. Customer experience is one of the most crucial elements in any industry. Companies are increasingly turning to advanced technologies to improve their customer experiences and remain competitive. 

Machine learning technology helps businesses use enterprise data effectively to benefit themselves and their customers. Combining data science and machine learning helps businesses use data to offer engaging experiences. A popular use case in this context is Facebook.  

Machine learning and AI can be used to provide personalized recommendations to people, depending on their preferences, location, and purchase history. Netflix, Spotify, Amazon, and other major platforms use ML to identify their users’ interests, allowing them to recommend similar options that may be relevant to them. 

In addition to this, machine learning also enables better management of customer support tickets. It helps provide answers to customer queries using natural language processing (NLP). Thereby saving significant time and resources for customer service agents as they don’t have to respond manually in most instances.

3) MLOps and DataOps for data management 

MLOps or machine learning operations and DataOps are significant use cases of DSML in enterprises. MLOps and DataOps are used in data management and strategic planning with AI, ML and data.  

These majorly contribute to enhancing customer experience and making applications smarter. A report by Deloitte estimates that by 2025, the market for MLOps solutions will grow from $350 million in 2019 to $4 billion.  

The Future Of MLOps: A Must Read For Data Science Professionals

4) Shortage of skilled data scientists and data engineers. 

According to Indeed, the annual average base salary of data scientists in 2023 in the USA and UK is $109,802 and £49,077, respectively. Data scientists are highly paid due to the transformation they offer your business. Data mining, cleaning, analysis, and transformation are crucial for business success, as data is gold.  

As a result, we will s3ee an increasing trend towards hiring more entry-level data scientists and machine learning engineers. 

The shortage of skilled professionals is also reflected in the rising salaries for machine learning experts. The average base pay for a data scientist has increased by 21% since 2017, according to Glassdoor. 

By 202, machine learning engineers and data scientists will be among the most sought-after professionals across all industries. 

5) More AI-based products

The market for AI-based products is growing bigger. More AI-based products will continue to emerge from smaller companies, as well as large tech companies such as Apple and Amazon. These products will solve specific problems in well-defined niches. 

From autonomous cars to autonomous anything, newer AI products built on ML will address all the human-run systems. Transformation and new trends in AI and ML in 2023 will make businesses become highly competitive. Your business can boost the value and quality of the existing traditional products and services by integrating AI-based technologies. 

6) Micro services and containerization will become the new normal for ML infrastructure. 

Micro services and containerization are two trends that have been gaining traction in the development world over the past few years. The idea is that instead of having one large monolithic application, you can have a series of smaller services (microservices) running inside containers that are built and deployed independently. These microservices can be reused across multiple projects, and they can be deployed in any environment. 

The same is true for machine learning applications. A microservice architecture makes it easier to scale your application by running multiple container instances in parallel. This allows you to better handle heavy workloads and reduce latency in your application. It also enables you to make incremental updates to your ML models without having to redeploy the entire application again.

7) Machine learning models will become more reliable, auditable, and interpretable. 

The next big trend we expect to see is the advent of more reliable, auditable, and interpretable models. Right now, we are still in a phase where most ML systems are "black boxes." The inner workings of these machine learning systems are hard to understand and explain. This makes them hard to audit and inspect for errors or biases that may be inadvertently introduced. 

We've already seen some great approaches to building more comprehensible models, including: 

  • Forecasting models that use linear regression under the hood 
  • Decision trees that can be visually inspected to understand the logic behind a system's decisions 
  • Generative Adversarial Networks (GANs) that can produce human-like text and images (though GANs are also quite unpredictable) 

However, we expect to see many more approaches like this being developed and deployed over the next couple years. 

8) Data privacy issues will get worse before they get better 

Data privacy issues will get worse before they get better. In the short term, it will be easy for companies to violate consumer privacy by accident or because of poor security practices. But eventually, consumer expectations and regulations will drive companies to take data privacy more seriously, resulting in significant changes to their business models. 

AI systems will become more aware of ethical issues — but whether they're actually more ethical is still up for debate. This is partly due to advances in machine learning, natural language processing (NLP), and other AI techniques, but also because technologists are thinking more carefully about AI ethics. 

Why is machine learning becoming significant? 

As businesses grow, their goals largely shift towards higher customer satisfaction, staying up-to-date, and ultimately becoming market leaders in their niche. Companies can achieve their goals with data or information relevant to them. 

Such data includes information about a business's customers, user behavior, buying patterns, competitor's data for benchmark analysis, and even customer needs and wants regarding a product. Statista's (2021) study shows that 57% of improving customer experiences represent major machine learning and artificial intelligence use cases. It proves that customer experience can be improved by incorporating data science and machine learning.  



Thanks for reading this far. I hope you found this post valuable—I know there’s plenty of information to absorb. And although the future is always hard to predict, and any opinions can change overnight, this list will help you prepare for what you might be facing in the years ahead. Whether it’s something you’re already doing today or something new that could emerge, there should be more than enough information here to make an educated decision about your machine learning strategy.  

Please leave your thoughts below and let me know what you agree with, or where you think I’ve missed the mark. 


Janaha Vivek

I write about fintech, data, and everything around it | Assistant Marketing Manager @ Zuci Systems.