By Laurence Liew | 28 April 2021
When AI Singapore started in June 2017, Laurence Liew, Director for AI Industry Innovation and AI Makerspace in AI Singapore was on a mission to help 100 companies build their AI products and solutions through the 100 Experiments (100E) programme. Starting with four AI engineers/data scientists, and co-workers from his previous organisations, Laurence shares how he has managed to build a 200 strong all-Singaporean AI Engineering Team with the Blue Ocean Strategy.
The classic management book “Blue Ocean Strategy” by Chan Kim & Renée Mauborgne describes the business environment using the terms ’red ocean’ and ‘blue ocean’.
Blue ocean strategy is the simultaneous pursuit of differentiation and low cost to open up a new market space and create new demand. It is about creating and capturing uncontested market space, thereby making the competition irrelevant.
In short, avoid the red market and hunt in the blue market where your competitors are not playing.
When AI Singapore started in June 2017, we were tasked to help 100 companies build their AI products and solutions through the 100 Experiments (100E) programme. We started with four AI engineers/data scientists, with co-workers from my previous organisations whom I managed to persuade to join me.
We put out an advertisement and received around 300 resumes, of which only ten were from Singaporeans. I only managed to hire one Singaporean to join our team – He is still with us today.
Being a government-funded programme and hosted by a local university, our salary structure and incentives could not match what the industry was willing to pay. How could we build up an AI Engineering team to meet our 100E KPIs when we had big tech giants such as Google, Microsoft, Grab, Facebook, etc., looking to hire the exact talent profiles in Singapore?
We decided to hunt in the blue ocean for these AI talents instead and cast our net far and wide.
AI Singapore launched the AI Apprenticeship Programme (AIAP)® in early 2018, where we deliberately hunted for AI talents using only two criteria:
- Must be a Singapore citizen.
- Pass the AIAP technical test and interview (read what we look for, test and interview).
We purposefully avoided stating the academic qualifications the candidate needed to have, such as a computer science or engineering degree with a specialisation in AI or ML. Instead, we stated the skills and knowledge the candidate needed to have to join the apprenticeship programme. We looked for talents that could be nurtured and developed in a short period of time.
In other words, we looked for passionate individuals who probably self-taught themselves AI/ML and Python (or R) programming and basic software engineering skills. They typically would have gone online and participated in various MOOCs or read up and practised their AI/ML skills on platforms such as Kaggle; were familiar with cloud computing and data management. Only some would have had formal education in AI/ML at the university level.
One of the biggest misconceptions that hiring managers have is that only Computer Science graduates can do AI and ML. Some of my best AI engineers and data scientists have degrees in economics, psychology, business, biology or industrial engineering.
What we have found is that the ability to learn fast, passion for solving data problems, and the love of working with data is key to being a good AI Engineer or Data Scientist.
Everyone can learn to program. Not everyone has a passion for data.
Why AIAP works is that apprentices get to work on real-world AI projects – not toy and syntactic datasets and problem statements. They get to experience what it takes to deliver an AI project to an actual customer – including all the pains of working with some demanding customers, missing or limited datasets and changing user requirements.
One of our pioneer batch of AIAP graduates who now works for the defence industry said: “There are many technical tutorials out there, but few offer the hands-on experience needed to address real-world problems, and that is one of the key differentiators of AI Singapore’s AI Apprenticeship Programme.”
Read the interview with Derek here.
This article was first published by Laurence on AI Singapore‘s website. AI Singapore is a national programme supported by the National Research Foundation and hosted by the National University of Singapore.
About Laurence
Laurence is the Director for AI Innovation at AI Singapore where he drives the adoption of AI by the Singapore ecosystem through the 100 Experiments, Makerspace and AI Apprenticeship programmes. He is also a visionary, serial technopreneur, and a veteran of the open source and HPC/Grid/Cloud community.
Connect with Laurence here.
