DIGITAL SKILLS FOR AI DEPLOYMENT - 2027/8

Module Overview

Knowing how AI works is one thing; making it work for you is another. This module is a practical "lab" where you build the digital infrastructure required to deploy AI in real-world scenarios.

We focus on the essential software skills needed to bridge the gap between raw AI models and standard engineering platforms. You will learn to use professional APIs to tap into cloud-based intelligence, manage complex datasets with precision, and automate the "boring" parts of analysis. By the end of this module, you won't just be a user of AI; you'll be building custom, automated toolchains that optimise workflows and turn data into actionable engineering insights.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.

Module provider

School of Engineering

Module Leader

HAGEN-ZANKER Alex (Sch of Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

This module provides the practical toolkit for implementing AI within professional engineering environments.

• The Modern Engineering Stack: Mastering the core ecosystem for physical data (e.g., NumPy, Pandas, PyTorch) and its integration into professional IDEs.
• System Interoperability: Scripting "bridges" between Python and industry-standard tools (e.g., CAD, BIM, or FEA simulation environments) to automate data exchange.
• API Orchestration: Leveraging external engines (e.g., OpenAI, Hugging Face, or Azure AI) to enhance engineering scripts with natural language or vision capabilities.
• Data Engineering & Pipelines: Building robust pipelines for cleaning and structuring "noisy" sensor data, ensuring high-fidelity inputs for inference.
• Professional DevOps for Engineers: Implementing industry-standard Version Control (Git) and reproducible environments to ensure code security and collaboration.
• Deployment, Interaction and Data Governance: Developing functional interfaces (e.g., Streamlit) and "machine-in-the-loop" systems that allow both human non-specialists and autonomous physical hardware to interact seamlessly with AI models. This is underpinned by a "security-by-design" approach to data governance, ensuring the protection and integrity of sensitive engineering data from the initial architecture stage.

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to:

• Develop the mastery of discrete skills, such as API integration, data cleaning, and automated script bridging.
• Develop and demonstrate the ability to coalesce the discrete skill in a full project mirroring professional practice

• Practical Coding Portfolio: A series of technical "sprints" demonstrating mastery of discrete skills, such as API integration, data cleaning, and automated script bridging. (LO1, LO2)
• Integrated AI Workflow Project: A capstone project where the student builds a functional, reproducible tool. The tool must solve a specific engineering problem¿such as automating a design parameter optimisation or creating a predictive maintenance dashboard¿and be managed via professional version control. (LO3, LO4)

• Live "Code-Review" Workshops: Mirroring industry practice, students receive feedback on the efficiency and readability of their scripts.
• Debug "Hackathons": Time-pressured sessions focused on troubleshooting broken pipelines to build technical resilience.

Module aims

• Develop the digital fluency required to implement AI within standard engineering software environments.
• Master the integration of external AI services and libraries into custom engineering scripts.
• Streamline engineering workflows through automated data handling and professional version control.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Employ a learn-by-doing framework
• Develop from conceptional architecture to full-stack implementation

The learning and teaching methods include:

• Architectural Lectures: These sessions establish the strategic framework for AI deployment. Rather than just syntax, the focus is on software design patterns, API orchestration, and the logic of scalable data pipelines. You will learn the "how-to" of connecting disparate engineering systems safely and efficiently.
• Intensive Implementation Workshops: The core of the module. These hands-on computer lab sessions act as a professional "sandbox" where you build scripts, connect to live APIs, and manage data. Tutors act as senior developers, providing real-time code reviews and troubleshooting guidance as you encounter real-world software hurdles.
• Collaborative Code-Reviews: Embedded within workshops, these sessions involve critiquing the efficiency, readability, and security of your peer's toolsets. This mirrors the professional DevOps environment and builds the critical eye needed for professional software engineering.
• Guided Independent Study: This time is primarily dedicated to the Integrated AI Tool Project. You will conduct independent research into specific software documentation (e.g., PyTorch, Azure, or BIM APIs) to solve a bespoke engineering problem, fostering the technical autonomy required for your later projects.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENGM332

Other information

The School of Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Employability: Graduates are prepared for the high-demand role of the "AI-Enabled Engineer." By mastering professional deployment skills, students gain the ability to lead digital transformation within traditional engineering firms.

Digital Capabilities: This module provides a deep dive into Software Engineering for Engineers. Beyond basic Python, it builds proficiency in API management, data engineering, and DevOps¿capabilities typically reserved for software specialists.

Global and Cultural Capabilities: Students learn to build resource-efficient code. By automating repetitive data analysis and optimizing simulation workflows, the module promotes the "lean" use of computational and human resources.

Resourcefulness and Resilience: Through the rigors of debugging and version control, students develop a "fail-fast, fix-faster" mindset. The module builds the resilience required to manage shifting software dependencies and complex technical debt.