Enginuity Future Skills Hub

The engineering and manufacturing sector is rapidly evolving, making it challenging for employers to identify the skills necessary to stay ahead. Future Skills Hub offers a comprehensive collection of resources on various technologies and their impact on skills demand, all presented in an accessible, bite-sized format.

What is machine learning?

Machine learning is a branch of artificial intelligence that enables computers to improve from experience, learning from historical and other ‘real-world’ data to analyse and make predictions.

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Machine learning explained

How is machine learning used?

Machine learning has various applications in manufacturing and engineering, ranging from optimising production and processes, to enhancing safety and personalising products and services. Examples include:

Enabling robots to learn and adapt
Analysing historical data for predictive maintenance and energy consumption patterns
Detecting anomalies and identifying root causes in manufacturing processes
Monitoring health and safety conditions in manufacturing environments.

Benefits of machine learning

Implementing machine learning applications in small to medium engineering and manufacturing businesses can lead to improved productivity, cost savings, and competitive advantages in the rapidly evolving landscape of .

Machine learning and Industry 4.0

Machine learning is integral to , empowering intelligent automation, predictive analytics, and adaptive decision-making. It enables autonomous systems to learn from data, optimise processes, and drive continuous improvement, fostering efficiency, flexibility, and innovation in manufacturing and beyond.

Machine learning and sustainability

Machine learning can promote sustainability by optimising the use of resources and reducing waste, predicting maintenance needs to reduce downtime, and enhancing process efficiencies, thereby contributing to reduced environmental impact and more sustainable manufacturing, production or operational environments.

What skills might you need?

Skills needed for machine learning include proficiency in data science, statistics, and programming languages such as Python or R. Additionally, expertise in machine learning algorithms, deep learning frameworks, and data visualisation tools is essential for developing and deploying machine learning models in engineering and manufacturing contexts.

Other specific skills include:

Data analysis
Artificial intelligence
Programming/software development
Data science
Solution architecture
Systems integration
Modelling
Innovation
Research
Governance
Problem management
Learning delivery
Data management
Security

What skills might you need?

Other specific skills include:

Data analysis
Artificial intelligence
Programming/software development
Data science
Solution architecture
Systems integration
Modelling
Innovation
Research
Governance
Problem management
Learning delivery
Data management
Security

What skills might you need?

While the core skills for machine learning will not be required for every individual in a company, fostering a general understanding of machine learning concepts and promoting a culture of data literacy can be beneficial. Machine learning skills might be distributed across these different levels and functions:

Machine learning specialists (data scientists, engineers)
Domain experts (engineers, quality control, maintenance)
Management (foundational understanding of ML concepts)
Business analysts and operations managers
IT professionals
Training and development professionals
Ethics and compliance officers
Human resources professionals
General workforce (data literacy)

Useful resources

Want to upskill?

You will find a list of online courses focusing on machine learning by following this link to MOOC

Digital Catapult

The Digital Catapult is accelerating the ethical and responsible adoption of artificial intelligence and machine learning. Find a range of resources, case studies and other information on their website.

Colleagues walking around factory together.

Dig a bit deeper?

Visit Skills for the Information Age (SFIA), the global skills and competency framework for the digital world.