My transition to becoming an AI/ML engineer in HTX’s Sense-Making and Surveillance Centre of Expertise from a materials engineering graduate.

Hey there, I’m Nigel! I’m a recent graduate from NTU’s Renaissance Engineering Programme and a latest associate with the Sense-Making and Surveillance Centre of Expertise (S&S CoE) under Home Team Science and Technology Agency (HTX). I’m a man-made intelligence/machine learning (AI/ML) engineer developing AI-enabled computer vision solutions and products.

My undergraduate degree is in materials engineering, which may be very different from what I’m doing now. I selected a profession in AI/ML as I had developed a powerful interest in the sphere through my two earlier internships with HTX. These opportunities helped arrange my technical foundation and realise my true passion, driving me to hitch HTX as a full-time staff via the HTX Associate Programme. I used to be delighted to be accepted by S&S CoE as an AI/ML engineer. It has been greater than two months since I joined the team, and the transition has been smoother than I had initially expected. In this text, I’ll share about my extremely fulfilling and rewarding journey with S&S COE, and maybe encourage you to take the leap of religion as well!

An enormous reason for my smooth transition is the team culture. My colleagues in S&S COE have been extremely friendly and helpful. They welcomed me and made me feel immediately like a part of the team since my first day. Guidance from them is forthcoming, and my colleagues are wanting to help me get in control. The open and collaborative environment in S&S COE has been a key consider my growth as an AI/ML engineer.

Moreover, S&S CoE has a powerful leadership team. Our Director and Deputy Directors are approachable and have a powerful vision for the team. They know what’s required of us and have charted a transparent path on how we might get there as a team. I used to be given clear personal goals and an upskilling plan through courses and overseas conference planning. While S&S COE’s leadership has ambition, they don’t put undue pressure on the team. We’re given sufficient space and autonomy to administer our work and learning. That is imperative as I actually have lots to learn before I will be as technically proficient as the remaining of the AI Engineering team.

While we work hard, the team knows the best way to have a good time too! We recurrently meet after office hours for fun activities like sports, having a drink or just hanging out. Recently, we did bouldering (passes sponsored by the corporate!), went for karaoke and can soon be going for archery tag (which I’m organising 😊).

S&S CoE truly looks like a second family to me. It is a strong motivating factor at work which makes coming to the office enjoyable, as work doesn’t feel like a chore. The positive culture and leadership of S&S are two big aspects which have made my otherwise daunting transition into AI/ML manageable.

On the surface, materials engineering and machine learning have little in common. Nonetheless, there may be transferable knowledge from materials engineering which has helped with my transition. The course gave me foundational analytical and mathematical skills which I still use today to assist me tackle AI/ML engineering problems. For instance, the linear algebra that I learnt remains to be useful in helping me understand the elemental math behind deep learning models. I also took a couple of programming and data science courses which gave me a great foundation in Python coding, data processing and machine learning methodology. Moreover, my prior internships with HTX exposed me to actual AI/ML work, allowing me to actually learn by doing. Collectively, these experiences shaped my considering and ready me for a profession in AI/ML engineering.

During my undergraduate days, I had learnt about Agile and Scrum processes, and their potential to revolutionise development teams. In my interview with S&S CoE, my Deputy Director described how the team was using such Agile process for Product Management. This impressed and excited me, as I relished the chance to practice Agile development in my first job. The promise of Agile methodology and Scrum processes turned out to be true. The teamwork and open sharing facilitated by these methodologies have benefitted less experienced engineers like me. Through Scrum, the team developed a scientific workflow, information sharing and strict documentation requirements. All these have helped me immensely, allowing me to realize proficiency faster than expected. I’m now a part of the InXeption product team, with whom I went through several 2-weeks sprints and made several useful technical contributions.

Model implementation

My transition to becoming a reliable AI/ML engineer is an ongoing journey. Up to now, I actually have learnt lots and achieved several personal milestones!

I used to be assigned to work on the Super Resolution module of the InXeption product. There are two essential components to this module, namely, the image quality assessment (IQA) model and the Super Resolution AI model itself. The IQA model would first assess if the photographs sent to the app would require enhancement, filtering out relevant images to the Super Resolution AI model for processing.

My first task was to benchmark various IQA models for classifying low- and high-resolution images. I evaluated 8 models from the PYIQA toolbox, using a custom-built dataset. I then accomplished the technical benchmarking, allowing me to pick the perfect performing model to be used. Finally, I dockerised the model and served it using Flask API.

I used to be also tasked to guage and dockerise an open-source Super Resolution model called CodeFormer (Zhou et al., 2022). To do that, I needed to read and understand the technical literature, replicate the outcomes and determine the perfect technique to deploy the “more complex” AI model. I actually have already accomplished all these tasks and am working on retraining the CodeFormer model. While this will probably be a challenge, I think it is going to allow me to level up as an AI/ML engineer!

Cultivating good software development practices

Code functionality and quality are necessary to make sure the sustainability of the code base. I wrote unit tests for the applications I built and used SonarQube to visualise the code quality reports and coverage of unit tests. My code was also evaluated using pylint to make sure that coding standards were adhered to. The APIs were documented using AppMaps to generate OpenAPI documentations and were tested using Postman. In keeping with the team’s practices, I also documented my learnings on the Confluence page to scale back the likelihood of others repeating the identical mistakes and the training time needed.

Finally, I deployed the Docker images of all my work on Government Business Cloud (GCC) AWS using EC2. Seeing the appliance that I had developed be finally deployed on cloud was undoubtedly a moment of pride for me.

My short stint at S&S has truly been rewarding, and I’m excited to see what else I can achieve with this team. Enabled by extremely supportive supervisors, I actually have already accomplished Andrew Ng’s Deep Learning Specialisation on Coursera and the Generative Adversarial Networks Specialisation in my 2 months here, while I actually have many other technical courses planned all year long. I’m also slated to go to 2 overseas conferences in Hawaii and Paris this yr to broaden my horizons and learn in regards to the trends within the AI/ML industry. Prior to this, I had never imagined the potential for going to an overseas conference inside my first yr of labor!

I sit up for the continued upgrading of my AI/ML technical skills through courses, projects and conferences. The variety of projects that S&S CoE is taking up is increasing rapidly, giving us junior engineers many latest, exciting avenues to explore. The team can be hiring, and I sit up for learning from our latest hires, experienced or otherwise. Empowered by these great opportunities with S&S CoE, I’m confident that I’ll grow to develop into a reliable AI/ML engineer in the long run!