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Ask the Career Advisor : COVID-19 and Your Job Search

Employing the right approach enables AI Apprentices to hack the job search process in these uncertain times

Lately, there have been quite a few queries by the AI apprentices on how they can succeed in their job search, given that they will be graduating into a recession of unprecedented scale caused by the COVID-19 pandemic. This is a valid concern. As the virus infection rates surpass 10 million cases worldwide, and with countries such as the US and Brazil in the throes of an infection wave while others like China and Australia reimpose lockdowns in their cities again in anticipation of a second wave of infections, the world’s economies, like cars that were caught in the flash floods in Singapore in June, are finding it difficult to restart again.

Businesses and companies in Singapore have not been spared, with those in the outward-oriented sectors the worst hit. Amid warnings of a spike in business cessations in the coming months and much of the world entering a recession that is expected to last at least 2 years, it is definitely not the best time to be searching for a job. Having left your roles to embark on this 9-month programme, you might have some doubts on how you can rejoin the industry when you graduate. The good news – there are reasons to be optimistic despite the gloom, and there are also creative, new methods that one can adopt to work around different challenges posed both by COVID-19, as well as the drying up of job opportunities due to it.

Confidence is Key

Firstly, it is important to always have a positive outlook, and to look at the situation as a glass half-full. Even when the unemployment rate is expected to rise to 3.6% (according to DBS Group Research [1]), we can see that the converse is true – 96.4% of Singapore residents will remain employed. Even if we accounted for underemployment and the long term unemployed, which has averaged less than 1% in the last few years [2], the figure would still stand at above 90%! It is also good to know that however discouraging the employment situation looks, there will always be sectors which are hiring (for example, the demand for food delivery services, telemedicine and e-commerce have all gone up due to the COVID-19 situation), as well as jobs that are in demand due to changes in how we do business and the advent of new technology.

Just what are some of these jobs? LinkedIn, the world’s most popular and largest professional networking and career development platform recently published its 2020 Emerging Jobs Report, which listed the top 15 jobs in various countries. The top job on the list for Singapore is :

A screenshot of a social media post

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It also lists Data Scientist, DeveOps Engineer and Data Engineer at numbers 5, 6 and 7, respectively. These are roles AI Engineers would also have the skills for. That’s the first piece of good news.

We can also validate this with actual hiring that has actually taken place recently. Let’s look at the hiring trends from LinkedIn’s Economic Graph data:

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Job titles that were most hired for in March 2020

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Job titles that were most hired for in April 2020

Even in the thick of the Circuit Breaker, more than 5.5% of all hires were for Software Engineers, a role that most of AI Engineers would not be entirely out of depth with. Finally, the proof is in the pudding : close to 80% of the AI Apprentices who graduated in June 2020 secured a role even before graduation!

With this confidence, it is still important not to rest on your laurels. What can you do to further increase your chances of securing that ideal role?

Research, Review and Update

You can start by looking through LinkedIn, job boards and career portals to research the skills and experiences needed for the roles you have in mind. Make a list of those, and think deeply about your past experiences – do you have those experiences? Or if not, do you have similar experiences and transferable skills? Would you have these experiences after the programme? Thereafter, you should thoroughly review and update your resume and your LinkedIn profile, to reflect these experiences and skills.

Network, Network, Network

Of course, one should not neglect networking, even during this period. Why should we do that, you may ask.

A large body of water

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That is because, like icebergs, much of the jobs actually available are hidden. It is estimated that as much as 75% of all open roles at any one time are hidden. These jobs may be outsourced to specialist recruiters retained by the company, are newly created or vacated roles which have yet to be advertised, or are open only to referrals by company employees. The only way to reach these hidden jobs, is to make yourself known to as many people as possible, and the best way to do that is to network.

Gone are the times people attend conferences, workshops and meetings to network in person. With COVID-19 lingering in our midst, networking is now done online. Get warm again with your existing network – interact with your LinkedIn contacts by liking or commenting on their posts, or even better, drop them a note to say “hi”. Make new connections by adding ex-colleagues, business associates, or even people whom you have met at informal settings before. Most people are more receptive to you joining their professional network on LinkedIn if they have met or interacted with you in person.

Increase your visibility and network on Linkedin by writing articles (on topics related to your work or professional interests), or join community groups available on the platform. Outside of LinkedIn, there are also in-person and virtual groups or communities you can join (some good groups with a variety of professions and interests include Lean In and Meetups).

One thing to note though, is to never approach networking with a specific ROI that must be reaped within a certain timeframe. Doing so will cause you to come across as disingenuous and insincere, a trait that you do not want your professional network to associate you with. That lady you chatted with at that conference, or that vendor you spoke with at last year’s project meeting may remember you for a new role in their company, or not – it does not matter. Your goal is to meet as many people as possible, share your professional experiences, and be positively remembered.

Parting Advice

Searching for and snagging your ideal role requires effort, but it doesn’t have to be onerous. If you need advice, you should always speak to a trusted mentor or a career development professional. Fortunately for the apprentices in AISG, both are readily available.


[1] Unemployment rate may widen to 3.6% by end-2020: analyst


The Full Stack AI Engineer

Recently, I came across this old article from Stitch Fix and it set me thinking about the kind of work we do here in AI Singapore and what it takes to be an AI Apprentice and progress on to become an AI Engineer. Continuing from my previous article, I think it is time to showcase the heroes of AI Singapore, our Apprentices and Engineers.

The Need to Go Full Stack

Typically, in an organisation, there are varied roles that are involved in building an AI solution. This includes, but is not limited to, titles such as Data Scientist, Machine Learning Engineer, Data Engineer, Infra/System Engineer, etc. Teams with such varied specialisations working together to create an AI solution is a common setup in many organisations today. This need often arises due to the lack of specific skills within the individual roles. However, some of these skills are actually not that difficult to acquire as long as the person has some basic level of technical competency.

Such separation of responsibilities tend to lead to higher communication overheads and coordination which often leads to friction that slows down the team. Contrast this with a software development setup where full stack engineers work across the spectrum of the technology stack and are often responsible for almost all engineering related tasks. Collaboration with other team members, mainly the UI/UX designer and Product Manager centres around user and product issues.

At AI Singapore, we adopt the latter style and differentiate ourselves from the current market where Data Scientists are solely responsible for the AI/Machine learning model while other engineers work alongside them to provide all the other necessary components that make up the full AI solution. Creating the AI model is only part of the overall bigger piece of work that is required to realise a working solution which produces meaningful outputs consumed by end users or other systems within a larger ecosystem. In the now famous paper of Hidden Technical Debt in Machine Learning Systems, one can get a glimpse of the various important pieces of components that are required to make an AI system work within a production setup.

Our AI Engineers are not just plain vanilla Data Scientists. They are what Stitch Fix would call a Full Stack Data Scientist. And going through our AI Apprenticeship Programme is what turns them into one. If someone just wants to learn the basics and theories of AI/Machine Learning, there are abundant courses and materials around that will teach you exactly that. However, when it comes to producing AI solutions that can be used by real end users in industry, the key differentiator our programme offers is the provision of the environment and the requisite training and guidance to achieve that. The initial 8 weeks of training bootstraps the Apprentices with the knowledge and prepares them to be ready for the full works of producing such a solution in our 100E and AI Bricks projects.

The Skill Set

In AI development, the following development lifecycle is something that is often seen (from Miscrosoft’s TSDP Lifecycle)

Coupled with the earlier diagram, one can see that this is a multi faceted effort which requires varied skills for the different types of tasks involved. So what are the skills that an AI Engineer needs in order to perform these tasks effectively?

At its core, the standard Data Science skills of data acquisition, exploration and cleaning is without a doubt the essentials. Just as equally important is of course the task of training, evaluating, debugging and tuning the models coupled with feature engineering know-how. Aside from these, engineering skills related to data acquisition (e.g. technology choice and usage), running modelling experiments plus tracking them effectively, code and model testing (using CI/CD pipelines) and finally deploying and integrating the model into the larger system form the rest of what I would consider Full Stack works.

As an example on Data Acquisition, the following could be some questions an AI Engineer needs to consider when building out the solution

  • Should the data be stored in a traditional database or a Nosql one like Elasticsearch? Or would some blob storage from the Cloud suffice? 
  • And how should the data be versioned? Should I use an external tool or does my data store provide that?

While these tend to be in the domain of what a Data Engineer would usually need to figure out, they are not exclusive to them. A typical Full Stack Software Engineer will be able to take on this equally well and the same goes for a Full Stack AI Engineer.

Another key area which often eludes the Data Scientist is the deployment of the model. This includes wrapping the model with an interface such as an API, building it through some platform which enables testing of the interfaces and the underlying code. We accomplish this with Gitlab as both our Source Repository and Continuous Development and Integration (CI/CD) platform. Apprentices learn how to write tests for their code and leverage the CI/CD platform to automate continuous testing of them and also trigger builds of the models they have developed and end it off with their deployment. 

Last but not least, the solution will also usually include feedback and monitoring loops that the AI Engineer will incorporate into the final product. Borrowing from the DevOps philosophy of owning the full development chain, such an end-to-end workflow is now commonly termed AI/MLOps.

Getting Started

If you would like to learn more about building end-to-end AI/Machine Learning solutions, Building Machine Learning Powered Applications from O’Reilly would be a good introduction. What I have briefly touched on here are just some of the common aspects of the kind of work that our AI Engineers and Apprentices do at AI Singapore. All these are of course made possible by our Infra and Data Engineering teams who build and maintain the essential resources and tools that the AI Engineers and Apprentices rely on day-to-day.

Sound exciting? Then nothing beats getting your hands dirty by becoming part of our family and experiencing it yourself! Ready to kickstart your AI career with us? Then go take a look at our becoming an AI apprentice article and its companion AIAP Field Guide.

My Not-So-Insane Career Leap: Chasing AI Dreams From My HR Job

Making the Leap of Faith, step by step

Foreword: As I wrote about my journey transiting from an HR Officer to an AI Engineer, I realised what worked for me may not always work for someone else. But I think my story is a little uncommon, so there might be something worth sharing here.

Fun fact: I graduated from NUS with a Social Science degree in Psychology and joined a HR team for my first job. Now, I’m an AI Engineer at AI Singapore.

To some, it seems like a complete 180-degree career switch: from a “soft/artsy” domain such as Psychology and HR, to a “hard science” such as Artificial Intelligence, Computer Science, Math. So, how did I end up here?

When I was a fresh graduate, I thought having a degree meant that I was qualified to change the world for the better. It was finally time to put 4 years of knowledge to good use.

Boy, was I wrong.

My degree was challenged by the workplace

In my first job, everyone was always busy. There was simply no time to step back and think about what we were doing. Everyone was just doing things because “it worked”. I even had a colleague who analysed data by printing spreadsheets and reading them row by row with a ruler. So much for living in the 21st century.

There was a strong disconnect between my workplace reality and the hyped up idea of a workplace in the digital age that I had came to believe in.

Being the millennial that I was, I feared the day where I would accept the status quo and live as a mindless office drone. I became frustrated with the situation I found myself in; I wanted to work in a digital workplace, but I couldn’t enable it. I was merely waiting for the digital workplace to happen to me. It was then I realised that to become an enabler, I needed to gain the right skills and knowledge. I started out by looking out for courses, training, and projects to level myself up. I stumbled, experienced failures, but eventually joined the AI Apprenticeship Programme and made a career switch to become an AI Engineer. This awakening was the start of my Data Science journey. Looking back, here’s what I think worked out for me:

1. Work smart by finding entry points into new skills from your current work

Transitions are hard, scary stuff. Not only was I juggling between work and self-learning, I had no coaches or mentors to provide feedback. I wasn’t even sure why I was coding what I coded.

I had to find a way to work smart.

I realised that HR and Data Science need not be mutually exclusive. What if I did both at the same time? I forced myself to integrate what I’ve learned in online courses and apply it to my everyday work.

Prior to my online courses, we normally used Excel spreadsheets to manage data and run analysis. After a few online videos, I made a deliberate decision to use Python for my analysis; Python was recommended as a skill needed by Data Scientists. Needless to say, I struggled with it so much that at multiple points in the project I was sorely tempted return to Excel. Why spend one week googling how to do sums, averages etc. in Python when I just needed a few clicks in Excel?

Obviously I missed my deadline for that particular week, but from then on I performed all my work in Python. I daresay I came out of the project a better programmer who could contribute more to the company.

Basically me after each project

By forcing myself to apply skills from Massive Open Online Courses (MOOCs) to my work, I managed to invest time to practise my new skills, without sacrificing time or quality of work. While the initial transition can be costly, we need to acknowledge that building skills takes time, and that we would only reap the benefits in the future. In fact, as I applied and practised more, my work became quicker and of greater quality than before.

Of course, this little trick is not limited to HR and administrative work, and Data Science as a skill.

Working in the finance sector? Try analysing stock market trends.

Mechanical Engineer? Try implementing a Computer Vision project for robotics.

I’m just suggesting from my own limited perspective of other fields, but I’m sure you get the point. Be creative and apply the new found skills into your work. You’ll find that, as you get practice in, both your work experience and skills improves.

Work hard, work smart, friends.

2. Be thick-skinned. Your first project will never be good, but it’s important to make the first steps

Around the time when I just finished my first MOOC, my Deputy CEO asked me to develop Machine Learning models to predict attrition within the company. I told him I was still learning, to which he replied, “You’re probably the most qualified in this room, and I’m asking you to try.” 😭

Was I qualified to do that? Probably not.

Did I succeed? Hell, no!

Armed with only 20 hours of training, I wrote about 80 lines of code that achieved ~60% accuracy in predicting who left or stayed in the company (slightly better than a coin flip). I ended up delivering a “machine learning model” that was really a PowerPoint slide hypothesising why the model was bad with no clear ways of advancing the project further. I didn’t know how to improve the model or deliver a product that the managers could use.

Even though I consider that baby project a failure, I learnt crucial and practical lessons that I didn’t get from a classroom setting.

You’ll strengthen your understanding only by trying out things and applying knowledge. What worked? What didn’t? Most importantly, what were the gaps you identified? The last question is especially important because it helps you to chart your next steps for improvement.

Adult learning is all about learning through practice (see: experiential learning). Be thick-skinned and accept projects, even if sometimes you feel like you are not ready for it. Your first product will never be good, but you’ll make iterative improvements to the project, to yourself. And that’s really the point; it’s all about trying, taking the first step, and letting the next few steps unfold with clarity. If you’re not already in the industry, this is the closest thing you can get to hands-on experience that everyone talks about in an interview. People want you to solve their problems, not impress them with textbook knowledge. So it’s important to get familiar with some of these actual problems they face. Get your hands dirty.

Of course, you need to be responsible and communicate to your stakeholders that you are still in the learning journey. Some bosses will probably still agree to letting you take on non-critical projects, but it’s good to be honest from the get-go.

3. Be your own coach. Let your experiences guide you as you transit

Starting out, I felt alone in my journey. My peers weren’t that much interested in it, and reaching out to established communities was daunting (e.g. Data Science SG, Girls in Tech Singapore). When practising on textbook examples, I wasn’t sure what I was doing; there was no context, no relatable goals, no problems to solve.

It was when I started forcing Python into my work that I began to realise I could try to solve HR problems with Data Science. By doing so, I could cross-reference my Data Science outputs with my knowledge from HR. The magic of learning a new skill comfortably in your own domain is that you can always refer to prior knowledge. This will be helpful in checking to see if the application of your new skill is correct.

Here’s an example: Let’s say I want to investigate how my salary compares to the rest of the organisation. I would use Python to load the data, calculate the average salary of my peers or officers in my department. Whenever my code churned out a result, I could always fact check with what I already knew. In this case, I know that the average salary was $X amount, so if my code returned $Y, I would know immediately if there was something wrong. This feedback was useful in getting me to relook at my code, find the errors, and learn from my mistakes.

Through this back-and-forth process of implementing and fact-checking with my prior knowledge, I had become my own coach and was able to build confidence in my new skills. As I advanced, I could ask deeper, more difficult questions, and eventually my skills also evolved to meet the (self-imposed) demands. My HR persona had become a guide for my Data Science self. I had started my transition into the Data Science domain by bringing my “HR -self” along in my journey.

My afterthoughts

Some expect change to be a sudden, Cinderella-esque moment when you wake up and suddenly you are living the dream. Reality and change itself are hardly that magical.

Where I think the magic really lies is in the constant upgrading of the self. Think of it as though you are playing a video game: you don’t really know if there is treasure in the dungeon, nor the dangers that lie ahead. What you could do instead to increase your success is to prepare yourself, get the right skills for the job. I believe that when you build upon yourself, you will be ready when the opportunity appears.

With that, I leave you with a quote from Neil Gaiman, and hopefully you will soon take your first step:

I hope that in this year to come, you make mistakes.

Because if you are making mistakes, then you are making new things, trying new things, learning, living, pushing yourself, changing yourself, changing your world. You’re doing things you’ve never done before, and more importantly, you’re Doing Something.

So that’s my wish for you, and all of us, and my wish for myself. Make New Mistakes. Make glorious, amazing mistakes. Make mistakes nobody’s ever made before. Don’t freeze, don’t stop, don’t worry that it isn’t good enough, or it isn’t perfect, whatever it is: art, or love, or work or family or life.

Whatever it is you’re scared of doing, Do it.

Make your mistakes, next year and forever.

Neil Gaiman, My New Year Wish, 2011

Top image : Celebrating Chinese New Year 2019 at AI Singapore

Inaugural AI Certified Engineer Awards Ceremony on 2 July 2020

It was not until as recent as the 1950s that the concept of Artificial Intelligence was expounded by a group of mathematicians, scientists and philosophers. Since Alan Turing’s 1950 paper Computing Machinery and Intelligence, the field has had an uneven journey of progress; with the advent of higher and less costly computing power, as well as better understanding of what AI can do, AI is now receiving a lot of attention from governments, businesses and scientists the world over.

Singapore is cognizant of the ability of AI to transform our businesses and lives; AI Singapore was thus established in June 2017 to harness the scientific and economic potentials of AI to develop local AI capabilities and grow the ecosystem for the country.  As a programme office, AI Singapore develops and runs national programmes, which includes AI Research Grant calls, AI Grand Challenges and 100 Experiments (100E), as well as our award-winning AI Apprenticeship Programme or AIAP®. 

Through the 100E, we assist companies in Singapore to solve their challenges through AI, and also help build up their own AI teams. While many companies do get off the launch pad through a 100E, they often report a challenge identifying the right people to recruit afterwards.  Modern AI tools and hardware have allowed many to learn to build and run simple AI models out of a laptop; however there exists a dearth of talents who can go beyond that, put AI models into production and deploy at scale. This became the motivation for AI Singapore to develop the AI Certification programme – a national assessment and certification framework to test and validate those who possess AI engineering capabilities and recognize them as AI Certified Engineers for companies in Singapore looking to hire such professionals.

The AI Certification Framework at a glance

To ensure the quality of eventual awardees and to earn the trust of the industry, the certification assessment has been designed with utmost rigour in mind – candidates for the AI Engineer Associate level (the first of 4 levels) have to pass a take-home assessment and thereafter, an interview with senior AI engineers from AI Singapore; those seeking certification at AI Certified Engineer Level I and Level II have to submit an in-depth technical report on the AI project/s they have embarked on in the course of their work, and attend an interview with a panel of senior AI engineers from AI Singapore and from the industry.

The journey for the awardees was not easy, but on 30 June 2020, we at AI Singapore were proud to officially award 76 certified AI Engineers, via an intimate virtual ceremony.

Together with the acknowledgement and support from our various industry stakeholders, which includes Daimler, Expedia, IBM amongst others, we are confident that these newly minted AI Engineers with their certified specialist expertise, will bring deep commercial value to the companies and organisations they join. Please join us in celebrating these individuals’ success and entry into certified AI Engineer fraternity!

The AI Certification is not an academic certificate conferred to one who achieves a minimum passing grade or score, it is a professional certificate awarded only to those who demonstrate the ability to deploy and deploy AI models in a commercial context.   If you have the experiences and have deployed a real-world AI solution – AI Singapore welcomes you to apply to AI Certification programme to get yourself certified.

An AI Certified Engineer certification will be a testament to your expertise, and also open doors to greater opportunities in the field.

For more information on the certification and the process, please go to

Data Engineering at AI Singapore

The newly formed Data Engineering team at AI Singapore has plans to refine data management practices in tandem with the growth in number of projects

Toward a Common Data Platform

The AI Innovation team at AI Singapore has evolved from a few staff to a strong collection of engineering teams in the last two years. We’ve already delivered several successful AI-based solutions to organisations of different types : government agencies, SMEs and multi-national corporations. Building AI solutions depends on data – a large amount of data. Data is the key project asset. Our engineering teams operate on various data formats : image, video, text, csv, etc. As our organisation has grown, so has the number of ongoing parallel projects. While we have put processes in place, the management of project data has primarily been the responsibility of the project manager and the technical leader in the team.

Data is an asset that must be managed and engineered to deliver value to end users.

Recently some of our senior engineers determined that we needed to move forward on a data platform programme that has been waiting in the wings for a while now. Our platforms team has also been deploying additional hardware at our site over the last few months which enables our senior technical team to think more broadly about evolving our systems architecture. The newly formed Data Engineering team will architect a common data platform that supports our AI engineers and facilitates efficient delivery of solutions.

Identifying Our Needs

At a high level, the tactical needs that our AI engineers have expressed include:

  • Data should be transferred between our external stakeholders and our project teams in a simple, secure fashion which provides tracking and notification.
  • Engineers should be able to view the holistic picture of the raw data sets and the data products and other artefacts that were created by downstream processes such as data cleaning, filtering and feature engineering.
  • The frameworks deployed and processes implemented should be oriented toward modern engineering practices and AI-oriented solutions.
  • Simple and efficient access to all types of data from interactive notebooks or processing pipelines.

Additionally, senior technical staff has expressed strategic needs that include:

  • Clear data governance practices of our current data inventory.
  • Data provenance and data versioning to enable reproducibility.
  • A common data model for metadata management.
  • ‘Right size’ our data platform : scope the effort and timeline to available resources while still addressing the challenges listed above.

There are many additional perspectives on data platforms on the internet. It seems that many are from vendors advocating for their products or describe a platform for a specific industry or purpose such as a customer data platform. As further reading, the descriptions provided here are more vendor agnostic:

The Next Steps

We have existing tools in place, both internally developed and open source, that currently support our engineers. Some were created opportunistically by a project team to address a specific need. A few open source frameworks were deployed to support certain types of projects. The technical leadership from the engineering groups are reviewing the current state. We may need to simply augment a tool, make more teams aware of how to employ it, or build processes that facilitate adoption. For gaps in our toolset, we will survey the open source and commercial solutions available for data storage, processing, governance and other tasks. The goal is an integrated data platform that supports each stage of the AI lifecycle : data collection, annotation, exploration, feature engineering, experimentation, evaluation and deployment.

As we begin to think about the scope of that effort and our near term priorities the data engineering team at AI Singapore will share our challenges and discoveries with the wider engineering community in Singapore and beyond. Hopefully, other data engineers can benefit from our experience. We also invite you to leave a comment if you have anything to share.

The Data Engineering Series

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