Machine Learning Trends and Predictions for 2024

By 2030, the global machine learning (ML) market, which was valued at $19.20 billion last year, could reach a whopping $225.91 billion.

As more companies incorporate ML solutions into their IT infrastructures, a vital question arises: what’s the most likely trajectory for the machine learning sector in 2024 and beyond?

Let’s answer this question with Symfa, a software engineering company with a knack for machine learning development.

 What’s in Store for Machine Learning in 2024?

Trend #1: Foundation Models Take the Lead

The foundation model term refers to large-scale machine learning models trained on vast amounts of data. These models can be fine-tuned or adapted for various tasks, such as natural language processing (NLP), image analysis, and content generation.

The concept has been popularized with the launch of ChatGPT— a large language model (LLM) solution developed by OpenAI. Also, The advanced ML tool can analyze input data against 175 billion parameters, demonstrating a profound understanding of the written text.

OpenAI’s product is one of the multiple examples of foundation models currently available. Also, Other popular solutions include the Synthesia.io platform for video content production; the DALL-E 2 image creation tool, the OpenCV library for object detection and classification; and miscellaneous open-source libraries, such as PyTorch and TensorFlow.

With foundation models, companies that consider implementing machine learning don’t have to train algorithms from the ground up for every process they’re looking to automate. Also, All you need to do is choose a foundation model that best suits your task — and train it on your data.

Thanks to foundation models’ reduced training efforts, speed of deployment, and reusability; they can become one of the leading factors behind machine learning adoption across industries, further fueling the ML market growth.

Trend #2: Machine Learning Platforms Dominate the Enterprise Tech Landscape

In the past few years, we’ve seen a clear trend toward the so-called “platformization” of machine learning. 

The concept revolves around turning ML capabilities into scalable platform as a service (PaaS) solutions. Such platforms allow developers, data scientists, and even business users without coding experience to train, deploy, and tweak ML models. 

What makes ML platforms particularly appealing is that users don’t have to understand the complexity of the underlying algorithms and infrastructure to accomplish their goals.

Other reasons for ML platforms’ growing popularity include:

• Democratization. More technology companies are striving to make ML solutions more accessible to a broader audience, not just to software engineers. And here’s where ML platforms come in useful. Let’s take self-service business intelligence (BI) tools, for instance. Previously, a company’s unit had to request descriptive or diagnostic analytics reports from in-house IT specialists — and wait for weeks until these reports were prepared. Also, Thanks to built-in machine learning capabilities, data platforms like Salesforce and SAP can generate such reports in minutes, allowing business folks to view and interpret the analytics results via sleek, user-friendly interfaces.
• Efficiency. Standardized ML tools and platforms reduce the time required for AL model development and training, eliminating the need to reinvent the wheel for every ML project in your company.
• Scalability. Popular machine learning platforms build on cloud infrastructures provided by Amazon, Google, Microsoft, and IBM. And this unlocks access to unlimited data storage and processing resources, which, in turn, helps companies scale machine learning solutions vertically and horizontally, accommodating the ever-evolving business needs.
• Reproducibility. One of the critical challenges in machine learning development is achieving consistently good results when applying algorithms to new data and tasks. A custom-trained ML model may boost breast cancer detection rates by 20%, but will your team be able to replicate the astounding results outside the lab walls? ML platform providers address this challenge by following machine learning best practices and standards, ensuring that the experiments and outcomes are consistent and reproducible.
• Cost efficiency. While it’s hard to estimate the cost of implementing AI solutions in enterprises without diving into your project’s details, most experts agree on the neat sum of $50 thousand for a minimum viable product (MVP) version of an AI-infused system.

Enterprise-grade systems:

Some examples of popular ML platforms include enterprise-grade systems like Databricks and DataRobot. Despite their robust capabilities, such platforms can be costly and tricky to customize. 

That’s why many ML adopters choose to create custom machine learning platforms using cloud services; such as AWS SageMaker; and Azure Machine Learning, combining them with open-source ML frameworks and libraries, including TensorFlow Extended (TFX) and MLflow.

Similarly to foundation models, ML platforms significantly reduce the barrier to AI adoption; empowering more companies to tap into advanced workflow automation and make better-informed decisions.

As businesses increasingly see the value in integrating ML into their operations; the demand for efficient, scalable, and user-friendly platforms will continue to grow. This will further propel innovations and improvements in the ML platform space.

Trend #3: Machine Learning Revitalizes the Internet of Things

The Internet of Things (IoT) is part of the intelligent automation tech stack in the enterprise sector.

With an approximate 15.14 billion installed base, the Internet of Things development; and adoption has nevertheless been hindered by persistent security risks, lack of standardization; and purely technical challenges like data latency.

By integrating ML capabilities into connected devices and systems; companies can reduce the time it takes IoT devices to gather sensor data; send it to the cloud for analysis, and receive a response triggering a particular action. Additionally, embedded ML solutions will help enterprises optimize cloud infrastructure costs by optimizing the number of server calls and the amount of data traversing the network.

Referred to as the artificial intelligence of things (AIoT), embedded machine learning, or TinyML; the technology is bound to boost IoT adoption rates in industries where faster response times are critical. Such sectors include healthcare, manufacturing, transportation, utilities, and construction. In just seven years, the global shipments of TinyML devices will exceed 2.5 billion units, with the market’s value reaching $70 billion. 

Conclusion:

While foundation models, ML platforms, and the convergence of machine learning; and the Internet of Things are the biggest ML trends for 2024; you should always keep your finger on the pulse of ML advances. Twelve months ago, no analyst could predict the rise of ChatGPT and its impact on the technology sector.

So, keep an open mind and remember: every technology, regardless of its business benefits; should be used responsibly and under the supervision of human experts!