The AIAP™ Journey : A Sharing from Batch 3

The nine-month journey of the AI Apprenticeship Programme (AIAP)™ ended on 23 December, 2019 for eighteen new graduates from the third iteration of the deep skilling programme. It was a packed 40-week period of their lives, but for many of them, becoming an AI engineer began a long time before that.

I was interested in AI during my final 2 years in university. When I went for interviews for actuarial positions, I was consistently asked if I had AI skills. I reckoned that meant that AI skills will be in huge demand in any industry in the near future.

– Xin Jie (graduate in actuarial science and statistics)

Some were fresh out of school and others had been out in the workforce for many years. What they shared was the belief that AI as a technology will become even more pervasive and has untapped potential to improve lives. What could be more exciting than actually having the chance to work on real-world data with real-world organisations to implement and deploy AI solutions?

A Community of Self-Starters

Joining AIAP™ meant being thrown into a sea of learning. It also meant being part of a community of folks who relish charting out their own personal learning journeys. What are the deliverables? How do they help the client? What relevant knowledge is required? What is the best way to acquire it? What should be proritised? Apprentices in the programme are not expected to sit around waiting for tasks to be distributed. In fact, they play an active role in defining what needs to be done during stand-up meetings which typically take place every other day in an Agile manner.

The programme offers a conducive learning environment with lots of self-directed learners who plan their own learning paths. If this learning mode matches well with you, this place is ideal! Else, this may be misinterpreted as a system lacking in structure and proper direction.

– Siew Lin

Of course, the apprentices did not simply work alone. Each project team, which consisted of two or three apprentices, was closely accompanied throughout the entire journey by a mentor who offered technical guidance and helped the apprentices to hone their critical thinking and problem solving skills. They were also supported by the steady and experienced hands of project managers who ensured that important considerations were not overlooked even as they buried themselves deep in the technical details for the best solutions.

Excellent mentorship makes working a breeze.

– Han Qi

As we enter the decade of the 2020s, work is becoming ever more knowledge-based. Lifelong learning has become an unavoidable part of one’s career. At current rates of knowledge generation, classroom-style learning no longer suffices. Network-based community learning will become commonplace. It was against this backdrop that the apprenticeship programme was conceived. Although the apprentices might be working on different projects in different industries, they were all housed under one roof which cultivated the development of personal relationships and catalysed the exploration of ideas along the hallways. These are important factors that make a vibrant technical community.

There was always someone around who could provide advice or technical help whenever we encountered a problem. Being in an environment where there are multiple teams working on different projects allowed us to bounce ideas off one another very easily.

– Benedict

Hardcore Techies

Git repo, EDA, PCA, Polyaxon, NER, CI/CD, unit test, Docker … these are but a small sample of the terms used in the trade that everyone in the programme became familiar with. There is no escape from the fact that the AI engineer has many concepts and techniques to grasp and the learning never stops.

The world of AI in the year 2019 was dominated by interest in transformer-based language models. The apprentices also took note of this development and self-initiated a series of sessions to read through and share insights on academic papers related to the subject, outside of their usual activities.

Every now and then, my peers are learning about the newest ML (machine learning) paper and sharing them. In a way, we are influenced by colleagues to keep up with the current trend.

– Kian Tian

Soft Skills are important too

While hard technical skills are often lauded, the human side of things cannot be neglected. At the personal level, it meant the ability to soldier on when things do not go as planned.

Resilience in experimenting with solutions; keep trying until a winning idea is adopted!

– Ken

Doing an AI project also meant participation in a team sport. Different players on the team have different mindsets and it is vital to bridge them.

Explaining difficult concepts to clients in layman terms is an important skill for technical communication since many clients (i.e. stakeholders and decision-makers) are often non-technical.

– Shao Xiang

With so many experiences gained in the preceding nine months, it is understandably difficult to convey the full takeaways in a single sentence. Perhaps the following quote comes close.

Too many good things to list – great colleagues, great opportunity, great brand name – all these allowed me to develop my data science skills rapidly and helped me get several offers before my graduation.

– Meraldo

The AIAP™ is the first TechSkills Accelerator Company-Led Training (TeSA-CLT) initiative in AI. This is a collaboration between AI Singapore and IMDA to develop a pipeline of AI professionals for the industry.

Application for the sixth batch of apprentices will open on 3 February, 2020. Click here to find out more.

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