After one and a half years of learning and working with Machine Learning, especially Deep Learning, I decided to stop. It is a hard decision in my life. Even though you might have heard the success stories in AI, I feel like it is not for me. Below are my personal thoughts about my journey.
1. Introduction
I began the AI trip in March 2020. Because of the nature of my learning ability, I self-taught myself and learned an immense amount of courses which are related to Linear Algebra, Statistics, Data Science, Machine Learning, and Deep Learning. With my knowledge, I was able to join a company that builds AI products. Eventually, at the beginning of July 2021, I returned to Software Engineering, which is my advantage and also my favorite.
2. Reasons
No journey comes to an end without the reasons, correct? Yes, I have two reasons for stopping.
The shorage of the job market for AI in Da Nang city
After deciding to change the environment, I found out that there were not many AI companies in Da Nang city. Then I started searching Facebook and Linkedin, but there were no available jobs.
The lesson I took away from this is always to find a suitable job market before you decide anything. If you want to follow AI or Data Science career, you should head to Hanoi or Ho Chi Minh cities because these places have greater opportunities for AI Engineers and AI Researchers. In my situation, I still have 2-3 years serving as a militia in my ward, so I am unable to leave the city.
My incapability in following research direction
Besides, from my point of view, I will not be thriving in researching because it is very challenging, and I also prefer the engineering and product building process. However, AI Engineering is somehow like Software Engineering because it similarly requires deployment, Docker, API Server, database, and so on. Additionally, you need to have the intermediate knowledge of AI like the list below:
- How to load a model
- What kind of input does a model require
- The format and meaning of the result
- Inferencing on GPU
- Modularize the code (optional)
- Convert a model into different formats
- Deploy models with resource efficiency
- …
These are the things that I have practiced many times. I am still aware of the steps even when I stopped two months ago.
So that is why I stopped, but now I am still applying AI knowledge to my pet projects. I still love it like the beginning <3.
3. Lessons
Following this field gave me many valuable lessons, like finding the job market above. The most valuable one is my learning style.
Alway begin a topic with a beginner course (from Udemy, Coursera, Youtube, or whatever platforms).
If you want to dive deeper, you can:
- Take the advanced courses
- Read the blogs, books, and articles
- Do complicated pet projects
Besides, I also gained confidence in Math (Linear Algebra, not sure about Statistics @@), AI projects; I also understood in which ways natural subjects can be applied to the real world. Thanks to these, I will not hesitate to dive into new things anymore.
Even though it is so tough, but it is enjoyable every time I look back. Learning new technology is the best thing ever. Thank you guys for reading my post.