4 Step Process to Level up Your Machine Learning Career Path
How I Went from English Teacher to AI Specialist
Hello Everyone!
I am Li Dan, a current AI Specialist at Yodo1. Before joining the company, I was an Algorithm Architect at Inossem software and a Senior NLP engineer in Baofeng TV.
I’ve focused my last 10 years on Natural language processing, machine learning, and deep learning algorithms and solved many problems, which include classification, clustering, entity recognition, keyword extraction, and many others, during this time. It never occurred to me to share my knowledge in any way, anywhere and anytime.
This is going to change today and with this series of blogs, I’m going to take you on a journey showing you a picture of AI technology as it stands today, helping my readers frame your business requirements into projects. But before that, I’d like to tell you what you may face as an AI practitioner through the lens of my experience.
Let’s start with my 4 steps multi-year journey. Ready?
Step 1: Pick a Single Topic and Stick to It! (2 Years)
As a beginner, I stayed on my first job for three years with only one focus — syntactic analyzing which includes sentence parsing, syntactic feature extraction, syntactic role tagging, etc.
On one hand, I formed a good working habit and knew better the pipeline of doing a machine learning project. On the other hand, I threw myself into a direction that few people go. This gave me the room that I needed to explore and find where I can add the most value so that I can get a better understanding of the direction compared to others.
If you skip this step and you keep doing shallow exploration, it would lead you to poor results.
Step 2: Take on Requirements Based Projects (1 year)
My second machine learning job was more requirements-driven and this is something I totally recommend you to look for.
More requirements mean more categories of needs and less developing time.
My knowledge of machine learning expanded rapidly during this period. But you must keep in mind that only a deep understanding of your project can make you more competitive. When you are busy with realizing business requirements, don’t forget to choose some which you are interested in to get an inside look in your spare time.
Step 3: Construct and Maintain an End-to-End System (1 year)
Don’t limit yourself within the strict line of algorithm engineer. Step out of the comfort zone!
Get to know how other modules work with your module. Ask yourself, is there any gap in there that both you and your college won’t realize unless one of you goes a step further to have a look. Grab the whole pipeline of the whole system as a product, and see whether there’s any optimizing space.
Step 4: Reconsider Your Goals (6 months)
After experiencing all the above, you may end up having many options and choices and feeling lost. It is okay to be a bit lost.
But it is at this point you need to reconsider where your ability and interests lie. Or, what changes you want to make. AI Specialist? System Architect? Senior product manager or Data Scientist? Decide for yourself and follow your own path.
Conclusion
Your career in AI won’t be built in a day. Be confident and be prepared to work hard. There is no magic formula, but these four steps will definitely help you improve fast and enjoy what you’re doing.
The purpose of the post is to introduce you to a world that is right in front of you and that you will not regret exploring. I can assure you it’s going to be worth your time, so join me on this journey and let’s learn AI together!
Thanks for reading :) If you enjoyed it, hit that clap button below as many times as possible! It would mean a lot to me and encourage me to write more stories like this
Let’s also connect on email
