Stepping out of my comfort zone and into the world of AI

Getting into the AI Apprenticeship Programme (AIAP) certainly isn’t easy. But for those who make the cut, a whole new world of opportunities awaits in the field of AI.

Shannen Lam had been working as a civil engineer for most of her life and was enjoying a stable job in a familiar trade when a sense of stagnation started to creep in.

After ten years in the industry, she decided to step out of her comfort zone and took up a Masters course in analytics at the Singapore Management University. The decision was sparked by an interest in exploring how data could be analysed to help with various situations at work.

While the course exposed her to machine learning models and equipped her with a theoretical understanding of the algorithms behind them, she knew that it was just the tip of an iceberg. “There were gaps in the know-how to implement a model, software skills essential for deployment, and exposure to other more sophisticated learning algorithms out there in the industry.”

When a friend recommended that she apply for AIAP, she decided to go for it. What attracted her to the programme was that she would get to work on real business problems with clients and have the opportunity to gain experience that she could put on her resume.

The decision made, Shannen would soon discover that “it ain’t easy to get in!”

As part of the technical assessment for acceptance into the nine-month deep-skilling programme, Shannen had to submit a solution involving data extraction, exploratory data analysis and an end-to-end ML pipeline.

While she had done Python programming in an integrated development environment, the challenge now was to put the lines of code into a pipeline script so that they could be executed from a command-line environment. She also had to organise her thought processes and present her solution to the technical assessment panel.

The rigorous entry requirements set the tone for Shannen’s AIAP stint, which began in March this year.

“The learning curve was steep. The courseware was structured to ensure apprentices develop a finer understanding of machine learning (ML) techniques and concepts and covered many different aspects of ML such as loss functions, data augmentation, and more. “I had to comb through the learning resources and digest them before applying them to problems that were assigned to us.”


In addition, there were weekly peer reviews of codes and presentations on topics that the apprentices had to research and present to the rest of the cohort. “The learning was not just from the mentors but also from fellow apprentices who had different coding styles and approaches to the same problem,” said Shannen.

Besides the coursework, a highlight of AIAP was the opportunity to work on 100 Experiments (100E) projects where AI Singapore engineers help organisations to solve problems for which there are no commercial off-the-shelf solutions.

Shannen was part of a team developing a network cyber attack classifier for a manufacturing client. The goal was to classify abnormal traffic detected in industrial controllers within the company’s manufacturing plant.

Being new to network security, she had to ramp up her knowledge through intensive research and literature reviews. But the project experience was gratifying. Apart from ML modelling, she had the opportunity to package and deploy the code in docker containers, write unit tests for function modules and use Gitlab extensively as a CI/CD tool. She also had the satisfaction of seeing the project through to fruition, with the ML model shipped as a working product to the end-user to achieve its intended purpose.

With two more months to go before she graduates from AIAP, the experience has taught her that there is more to an AI solution than building and tweaking ML learning models.

“Most of the courses out there focus on building and perfecting the algorithms. It was only after I entered AIAP and was involved in an actual ML project that I was exposed to other dynamics that are equally important in a project lifecycle, such as business expectations, data integrity, building a reproducible pipeline for sustainable ML training and experimentation, and so on.”

“Going through AIAP has helped me to build my skillsets and confidence in meeting the needs of the industry,” she said. “It has also connected me to a network of like-minded friends and given me a good start for a career in AI.

To find out more about the AI Apprenticeship Programme:

Join the AIAP community today: