Five machine learning trends that leaders can take with them from 2024

As artificial intelligence (AI) and machine learning (ML) dominate headlines and reshape industries, they are not just buzzwords – they are revolutionizing the way we work.

The year 2024 is coming to an end, Cambridge Advance Onlinethe University of Cambridge’s online short course provider, leverages academic leaders’ expertise in AI and data science. Dr. Russell Hunterto uncover the key ML trends business leaders need to know as they navigate this rapidly evolving landscape.

This is due to more and more UK companies integrating AI into daily operations and more and more British professionals pursuing a career in ML:

• IBM’s latest global AI adoption index found that 42% of large companies say they are actively using AI in their company – the same number who were investigating its use the year before.
• Recent Google Trends data suggests a 30 percent increase in searches for machine learning jobs month-over-month, while searches such as “how to become a machine learning engineer” and “machine learning engineer jobs” have increased over the past five years Increased interest by 300%.
• In addition to the term “machine learning,” questions commonly asked about entering the industry include: “What are the basic requirements to learn machine learning?” and “Should I go into AI or ML?”

Dr. Russell Hunter works in the Department of Engineering at the University of Cambridge and leads Cambridge Advance Online Using big data for business intelligence Course.

ML operations

ML operationalization management – ​​or ML Ops for short – focuses on deploying, monitoring, and controlling ML models in production. “In the early stages of our innovation work in this area, there were concerns about performance drift, managing multiple model variants, and retraining new data without impacting the business,” says Dr. Hunter.

“This is the type of problem that ML Ops can help solve, as it integrates best practices from a well-established DevOps practice to ensure the reliable and scalable operation of ML systems.” Standardizing and streamlining ML workflows through ML Ops has become essential as companies expand their AI capabilities. This trend has cemented its place in the industry and enables faster deployment and maintenance of ML models.

Autonomous decision making

These advanced systems are transforming industries by accelerating the speed and precision of decision-making, increasing efficiency and improving the customer experience. By automating manual processes, ML technologies can improve companies’ ability to quickly analyze large amounts of data, uncover patterns, and make informed decisions.

Dr. Hunter explains how autonomous systems can be used in industries such as healthcare: “Sophisticated multimodal AI can analyze genetic data and patient histories to recommend personalized treatment plans. This leads to more effective and personalized healthcare. Similarly, by leveraging data from electronic health records, these systems can predict patient outcomes or complications, allowing for proactive intervention.”

Quantum machine learning

“As AI continues to grow and evolve, the computing resources required are also growing exponentially,” notes Dr. Hunter. This pioneering area is attracting significant research and investment activity, particularly in key industries such as finance and pharmaceuticals, as well as big names such as IBM and Google.

Dr. Hunter continues: “Quantum AI has the potential to enable more accurate and complete models because they are not limited by classical computing. This is rather speculative for the future, but it is an exciting area and has the potential to solve problems beyond the reach of classical algorithms.”

Edge AI

Another groundbreaking development, Edge AI provides instantaneous processing capability critical for applications in autonomous vehicles, industrial automation and health monitoring where time-critical tasks require rapid responses. According to Dr. Hunter achieves this by processing data locally on the device, reducing latency, enabling real-time decision-making and minimizing the amount of data that needs to be transferred to central servers.

Processing sensitive information locally also improves privacy and security and reduces the risk of data breaches in transit. However, Dr. Hunter points out that “challenges such as hardware limitations, integration complexity, and the need to efficiently manage and maintain numerous edge devices limit the full effectiveness of edge AI.”

Expanded workforce

Although there are concerns that AI will replace humans in the workplace, Dr. Hunter says the latest AI developments can enhance rather than undermine human contributions: “The augmented workforce trend uses AI to support rather than replace human workers, transforming work roles and increasing productivity” across various industries away.

“This collaboration between humans and AI combines the strengths of both, allowing AI to handle repetitive, data-intensive tasks while humans focus on strategic, creative and interpersonal activities that require emotional intelligence and critical thinking. Instead of eliminating jobs, AI is reshaping them, leading to the creation of new roles that require managing, programming and collaborating with AI systems.”

As a business leader, it is important to keep an eye on these developments to ensure your company is well-equipped to gain an edge through the use of AI and ML.

For a detailed look at these findings and other trends, read Dr. Hunter’s Machine learning trend analysis on the CAO blog.