Giving up a promising full-time job in banking is not an easy decision for anyone to make. But for Kenneth Wang, a Business Administration graduate from the National University of Singapore, it was a risk worth taking, for the opportunity to “future-proof” his career.
Kenneth had been working as an associate with Standard Chartered Bank for two years, rotating across different wealth management functions before taking on a permanent role with the Managed Investments team responsible for the distribution and due diligence of mutual funds and exchange traded funds.
To better analyse the performance of various financial asset classes as part of his investment research, he took up Python programming and machine learning (ML) courses on Coursera.
As his interest in these areas grew, he started to consider a career AI/ML.
“I felt that AI/ML could lead to many job opportunities across multiple industries,” he said.
Studying the job descriptions of various AI/ML roles in Singapore, he noticed that employers were looking for individuals with industry experience. He also found that AI Singapore’s (AISG) AI Apprenticeship Programme (AIAP) was one of the few programmes that provided that experience, with a project phase where apprentices get to work on real-world business problems.
In 2019, Kenneth decided to quit his full-time job to join AIAP, despite the uncertainty of landing a full-time role after the apprenticeship. It helped that his family and friends were supportive. “They were aware of the benefits of AI/ML and felt that the skillsets acquired through AIAP will help to future-proof my career,” he said.
In preparation for the programme, Kenneth reached out to his seniors from earlier batches to understand their experience and get their feedback on AIAP. He also turned to Coursera, Udemy and Fast.ai for courses to bring him up to speed on Python programming and AI/ML concepts.
The groundwork proved to be important.
To keep up with the content, he would document learning points or code snippets on a digital notepad for future reference. He also tapped on the knowledge of his mentors and fellow batchmates, and weekly mentoring sessions served as useful checkpoints to internalise what he had learnt and gave him the opportunity to voice out any difficulties that he had.
The highlight of AIAP was the project phase, where he had the opportunity to work on an open-source information retrieval tool “Golden Retriever” for human language queries. Golden Retriever was part of a set of pre-built solutions offered by AI Makerspace to make it easy for teams to integrate AI into their services.
What Kenneth valued, in particular, were the sessions where apprentices came together to share their learnings from the projects and the difficulties that they faced.
“The sharing exposed me to the different areas and applications of AI/ML,” he said. “It was through these sessions that I realised there are multiple ways to solve the same problem, and the best solutions are usually discovered by gathering ideas from different perspectives.”
One important lesson was that “no problem is too big when you break it down into smaller pieces”.
“It is often easy to be intimidated by the huge problem statements that your clients or managers bring to you. Through the project phase of AIAP, I acquired the ability to break complex problems into smaller parts, which makes it easier to test and verify solutions with end users,” he said. “This helps to prepare me for larger business problems that I will encounter in the future.”
A few months before graduating from AIAP in June 2020, Kenneth managed to secure a role with AISG, joining the MLOps working group responsible for developing new tools to automate and monitor the ML pipeline. Part of the job’s attraction for him was the AISG culture where junior developers are able to take ownership of their projects across the full development cycle, while being able to tap on the knowledge of senior managers across different technical functions.
Kenneth is currently working to integrate Gitlab continuous integration/continuous delivery (CI/CD) and data version control with AISG’ internal experiment tracking tool to make ML models and datasets more reproducible.
Looking back, he is thankful for how AIAP has helped him to grow his understanding of AI/ML concepts and develop software engineering skills to build AI systems. It has also given him access to a network of AIAP apprentices whom he can interact with and seek guidance from. “Coming from different industries and backgrounds, the apprentices provide differing perspectives to the problems that we face as AI engineers,” he said. All these have proven to be invaluable as he continues to build up his capabilities and make progress in his AI journey.