Four Top ML Trends to Adapt to for the Future

Check out four ML trends that are disrupting every industry.

March 13, 2023

ML Trends

We’ve come a long way since 1943. Even then, this pioneering development demonstrated computers could communicate without human interaction. Find out about the latest machine learning (ML) tools, software, and best practices disrupting businesses across the board as you plan your year, says Lucas Bonatto, the CEO & founder of Elemeno.

This was just the beginning. Over the next six years, the global ML market size is expected to rise at a CAGR of 38.8%Opens a new window , from $21.17 billion in 2022 to $209.91 billion by 2029. The anticipation of this growth means that in 2023, organizations will see a paradigm shift in how they prioritize ML investments. 

On average, companies report using six different toolsOpens a new window for model building and training, with executives focusing more on downstream ML capabilities, like observability and feature governance. The move from building company-wide, complex ML models to smaller, task-focused models increases their transferable use and lowers barriers to the market. Startups combining pre-trained models with a greater user interface—meaning you don’t necessarily need coding experience to manage them—are already disrupting entire industries. 

ML is growing rapidly, so let’s explore the trends for where it is going in this year ahead.

Generative AI

Trained by everything from books and podcasts to satellite and internet of things (IoT) data, generative artificial intelligence (AI) can create new content, including audio, code, images, text, simulations, and videos. It utilizes deep neural networks of billions of parameters to enable complex pattern recognition. 

The unsupervised or semi-supervised learning algorithms are making huge strides to accelerate research and development (R&D) cycles in medical and financial forecasting fields. For example, OpenAI’s generative AI has been praised for its slick ability to write complex code and pass an MBA Operations Management course with a B minus through producing a final exam essay. 

As such, these tools also open up new avenues for fraud. Already hacker novices have taken advantage of generative AI as it lowers the bar to code generation for intelligent cyber attacks. 

One thing is for certain: The sector will be increasingly regulated. The EU AI Act, American Data Privacy and Protection Act, and Securing Open Source Software Act are all cracking down on conditions to encourage the safety and security of modern tech-driven lifestyles. Whether your firm has entered the ML world or not, businesses must be aware of these acts and plan strategies for strengthening fraud detection to mitigate risks against the latest ML tools.

Computer Vision

Computer vision (CV) takes the largest share of the AI and ML market. It is a field of AI that can capture, process and analyze real-world images, enabling meaningful, contextual information extraction. 

One sector where CV is making an impact is the automotive industry. It can detect defects in the body of vehicles and underpin the development of applications such as self-driving cars. High-definition cameras with background CV systems identify surrounding objects, people, and movements that automatically trigger the car’s response. 

More than 60% of vehicle defectsOpens a new window found at the roadside could have been reported and fixed before beginning a journey. CV will increasingly help maintenance providers perform inspections effectively and thoroughly by using cameras to identify dents and mechanical parts out-of-place. With a CV, engineers can process images and identify discrepancies within seconds. 

From autonomous drones to automated retail stock checks, CV is reducing lead times, workers’ effort, and operational costs across the board.

See More: How AI and Computer Vision Shape Our World

Cross-industry AI Synergy

ML will be less about company-specific models and more about data-centric models with transferable uses across sectors.

Look at healthcare. Doctors and scientists have experimented with ML and CV technology, training it to recognize and classify rare genetic skin conditions. 

Now think of a grocery store. Professionals pace the aisles, in some cases, hourly, to take stock counts and ensure product availability. But what if they put CV applications on shelves to help keep real-time track of inventory? 

As companies begin to share investment costs on tools that can analyze visual patterns and detect anything from rare diseases to product movements, more experiments and affordable models can be created. 

Late adopters are increasingly looking at use cases of models from more mature ML industries, such as automotive and healthcare, and adapting them to support their business needs. With data-centric ML models on the rise, 2023 will show more cross-industry synergy, using data to differentiate the model’s purpose. 

See More:AI- and ML-based Forecasting: Demystifying Emerging Technologies for Business

ML Data Scientist Upskilling vs. Low-code solutions

The US Bureau of Labor Statistics estimates the global shortage of software engineers could reach 85.2 millionOpens a new window by 2030. The solution to these fears: don’t code at all.

No-code and low-code (LCNC) platforms allow users with or without programming language knowledge to operate and build ML tools through intuitive interfaces such as point-and-click and pull-down menus. This lowers the barrier to ML, allowing businesses to create tailored applications at a considerably cheaper and quicker rate.

The use cases for ML are expanding rapidly as developers start to reinvent workflows based on what the technology can deliver. AI natives can re-engineer systems, pre-programming them to proactively push alerts and red flags to users when certain triggers are hit. Looking through the lens of defect detection, the computer can say, “Hey, you missed this one,” or “Incorrect classification,” while the team validates the machine’s findings and governs and trains the tools.

Nevertheless, LCNC tools are fundamentally limited in customization scope by design. Highly skilled software engineers will be required to build, monitor, and scale these platforms. These latter needs are likely to give rise to a demand for distinct new roles, such as human-computer interaction managers. 

It’s safe to say that no matter what industry or ML maturity, your business has already been impacted by ML in one way or another. Business executives should look closely at the exciting solutions generative AI, computer vision, and low-code solutions offer while remaining cautious of how these tools could present challenges. Whether you opt for an in-house data expert or outsourced support, trends for data scientist upskilling and low-code solutions soar in 2023.

Which challenges have you faced while implementing ML models? How have you rectified them? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

Image Source: Shutterstock

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Lucas Bonatto
Lucas is a technical founder who studied Computer Science and is currently leading Elemeno AI, a startup helping data science teams to increase their output in the industry. Lucas has experience working in a wide range of industries, including finance, retail and crypto. He is passionate about the advancements that AI could bring to our lives, and believes that human beings are happier doing creative tasks.
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