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 changing the way we work. These technologies are expected to continue to evolve and continue to influence many aspects of our lives.

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. As 2024 draws to a close, these are the key ML trends business leaders need to be aware of as they navigate this rapidly evolving landscape:

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.

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.

Advanced multimodal AI can analyze genetic data and patient histories to recommend personalized treatment plans. This leads to more effective and personalized healthcare. Likewise, by leveraging data from electronic health records, these systems can predict patient outcomes or complications, allowing for proactive intervention.

As AI continues to grow and evolve, the computing resources required also grow exponentially. This pioneering area is attracting significant research and investment efforts, particularly in key industries such as finance and pharmaceuticals, as well as big names such as IBM And Google.

Quantum AI has the potential to enable more accurate and complete models because they are not limited by classical calculations. This is rather speculative for the future, but it is an exciting area and has the potential to solve problems beyond the scope of classical algorithms.

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. This is achieved by processing data locally on the device, reducing latency, allowing decisions to be made in real time 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, 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.

Although there are concerns that AI will replace humans in the workplace, the latest AI developments can enhance rather than undermine human contributions. The augmented workforce trend uses AI to support human workers rather than replace them. This will change work roles and increase productivity in different sectors.

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 Cambridge Advance Online Blog.

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Dr. Russell Hunter is the academic lead for AI and data science at Cambridge Advance Onlinethe online short course provider from the University of Cambridge. Dr. Russell has a PhD in Computational Neuroscience and moved from academia to industry. Today he works as a technical lead in the areas of web development, serverless architectures and machine learning. After eight years as a postdoctoral researcher in Cambridge, where he developed educational apps for aspiring engineers, his career spans software engineering, image processing, edtech and lecturer in computer science.