Can I Just Go Home Now?
This part of my day was not fun.
Remembering it is not fun either, in fact, writing about it is also not fun.
But I said I was giving all the full details, and I am.
Here we go…
It’s lunchtime, and I go out to pick up some tofu, rice, and peanut sauce (you probably didn’t need to know what I had for lunch lol). I usually eat alone and ponder the mysteries of the Universe, but on my first day, the Universe can wait.
So I chose to eat with my new team on a shared desk with everyone’s laptop and electronic devices on it — can you already see where I am going with this.
After 20 minutes of chats, laughs and loud chewing (from me), I finally reached over to grab my full glass cup of water.
As I reached for my glass, suddenly, my hand-eye coordination and brain calculation of how much force should be exerted to pick a glass seemed to malfunction, and I knocked over the glass of water.
I kid you not, but I saw this event unfolding in slow motion.
There pure terror on my face as the water rapidly spread over the whole table, reaching everyone’s laptops and devices, and finding its way to the extension cables.
I shouted (to be honest, I actually screamed).
Everyone jumped out from their chairs, placed their lunch down, and went to the nearest tissue and towels they could find. It all played out like some reality game show activity.
Everything turned out fine in the end, laptops and phones were dried, floors were cleaned, and we all just laughed it off. Thankfully, my new teammates are somewhat forgiving.
Advice: If you are clumsy like me, practice eating and drinking before your first day. Obviously joking here.
After the excitement from lunch, and my body receiving a jolt of adrenaline from the unfolding events, I was ready to get back to work.
Before my first day, I had been communicating with the CEO/CTO of the company to understand what they required from me as a Computer Vision Engineer.
I had an idea what their long-term vision was, and I knew what I was brought on board to achieve.
Taking the company’s goals in mind, my afternoon proceeded with an exploration of research papers and GitHub repositories on techniques such as pose estimation, hand tracking, and object detection. I also looked at how these CV techniques were to be leveraged within mobile devices and in augmented reality environments.
Here is a quick article I wrote that explains some conventional CV techniques that you might encounter in working environments.
Artificial Intelligence is a fascinating field, with the boundaries of what’s deemed possible pushed daily. We, as AI enthusiasts and practitioners, love to read and research into AI-related topics. But in practical environments, what really matters is the ability to engineer these techniques into commercial applications that are to be utilized by hundreds, if not thousands of people.
So my first day brought the understanding that my role as a Computer Vision Engineer has a split of 70% engineering CV techniques and 30% research. And I am pleased with this balance.
Advice: Understand where you want to be in your career 10 years from now before settling down in a long term job.
Advice: Practice utilising ML models in practical scenarios. Deploy ML models as mobile and web apps and think of how everyday people can use your ML models.
Advice: Understand the vision of whatever company or organisation you are becoming a part of, and know where you and your skills fit in.
Advice: It is helpful to conduct research into what you will be working on before your starting date, as this will convey a sense of dedication and motivation from yourself to your colleagues.
Late Afternoon / End of Day
With a clear understanding of what my role entails and what technologies I will be working with, I had a solid foundation to build upon.
Towards the end of the day, I worked on some documentation and admin tasks. There was no code written or research papers read, but none the less this part was as important as any part of my day.
I conducted an observation into what sort of environment I would like to work within, and I concluded that I would like to work in an environment where everything (or at least 90%) of the role was documented. And I mean everything, including documentation on hardware and software specifications, documentation on what Machine learning techniques are utilized in applications, and documentations into the Machine learning workflow.
All these are important, as it makes understanding and explainability of my work more accessible to my teammates and also future me. Documentations would come in handy if I were ever retrospectively to look back to previous work or decisions made.
With folders and documentation created, I was happy with what I had achieved on day one as a Computer Vision Engineer.
Bring on day two!