How I Transitioned into a Machine Learning Engineer Role (In Just Two Months, After A Long Career Break)

In this article, I will share my journey from being a stay-at-home mom to a Data Wrangler to “Machine Learning Engineer (NLP)” in 2 months! My goal is to inspire others in a similar situation to take up a similar path. The journey helped me to get high-paying job at one of the largest companies in the world.

Background

I was trying to re-enter the workforce after a break from maternal care. Soon realized there are many challenges for me. And had to deal with

• Knowledge of outdated technology,
• Low confidence due to age factor,
• Not having a proper direction on where to start,
• Lack of proper skill set, and
• Inherent bias among recruiters

How did I get started?

I started with a conviction of not settling down for less, for what it’s worth. Then Googled for jobs in high demand. The most common results were of Machine Learning Engineers, Data Scientist and, Data Analyst. So why not give it a try! After some research again, I found technologies that were required to venture into all these jobs. Python, C++, R were top contenders and among them, Python was my area of interest.

Now, I knew what I want, where can I learn these?

Youtube and FreeCodeCamp had some resources that were free among others. I enrolled in a Python course. Thereafter, it was a constant struggle to learn to code. There was no one I could ask for help which led to frustration and depression. I researched, joined many community groups for Python and Data Science. In the end, everyone parts away after training is done.

Struggling to code alone is the most difficult part for self-learning beginners.

Then, one day, a good friend introduced me to Omdena. It was a life-changing experience.

Omdena to the rescue!

Omdena does AI/Machine Learning projects. It is an organization that builds AI for social good. With the little real-world coding experience and half-baked Machine Learning knowledge from free Machine Learning bootcamps and Youtube, I managed to pass the interview. I was given a role named “Data Wrangler”. Here is the link to Omdena job roles.

I joined a project named “Fighting Misinformation and Promoting Plurality by Detecting Fake News”.

A team of 44 AI Engineers from 20+ countries also joined the team. Team members were a mix of established career professionals (10 years or more experience) who are transitioning to Data Science, people with 4 or 5 years of experience who are already working as Software Engineers /Leads wanting to take a quick shortcut in learning Machine learning/AI algorithms. Some were Ph.D.’s and pursuing undergraduates in some AI Specialization, job hunters, and a small portion were stay-at-home moms like me. The level of enthusiasm exhibited by these team members in solving misinformation on the Internet was at an all-time high.

But, I did not fit in!!!

Growing into Machine Learning Engineer

To start off and not be an embarrassment, I wanted to utilize the ‘Data Wrangler’ role that I was given. Tried to code with whatever knowledge of Python I had. Then understood that Machine Learning algorithms work only with numbers and that is what the free Machine Learning Bootcamp trained me.

But, this project contained text data from news websites. How do I convert text to numbers? It’s a whole new world all over again!! After some deep breaths and some research, I understood what Natural Language Processing (NLP) was. Reached out to project members for help with hesitation. To my amazement, I would receive replies from at least 5 to 10 team members reaching out to help with ideas and resources. Learned from them. How to use new software and tools. Looked into their code. My confidence got a boost. I stayed in my zone and stuck with Data Annotations.

I got curious and wanted to push my boundaries further and yearned to write a Machine Learning model. Reached out to team members again, asked a lot of questions, and got directions. Each one of them was patient, kind, and respectful to answer my queries and took their time out to teach me. All collaborators in the project contributed to each block of code that I wrote myself from scratch.

How I made it

It was hard. I had to juggle between commitments from data annotations, model development, child care, and homeschooling kids due to pandemics. Every minute devoted to coding was precious. It wasn’t possible during the daytime due to family obligations.

The dataset provided by the client was massive(which is normal for any Machine Learning project) and the annotation task took a big chunk of time. Time was precious. I will lose the opportunity of mentorship from my team for machine learning model implementation if I fall behind. And, so I sacrificed sleep time. I got a continuous block of time to stay focused on coding during nighttime. It worked out! After 2 weeks of sleepless nights,I finished my first Machine Learning algorithm for identifying a sentence in a news article that had Hate Speech in it.

There were so many mistakes. Again, team members were kind enough to redirect me in the right direction and share resources. I persevered and, in the end, Implemented 2 machine learning models for Hate Speech Detection in a news article. Finally, became a Machine Learning Engineer. What an unbelievable good start for restarting my career!

And,all this happened in 2 months’ time which is mind-blowing. There I was, going from stay-at-home mom to Machine Learning Engineer specialized in NLP in 2 months and it’s free!.

Omdena Support

I built a professional relationship and community of Machine Learning Engineers because of this Omdena project. Lean on them for resources even after the project is done. They will be my support when I’m stuck with code or need help. I found a community where I can ask or share career-related concerns. This is the first step for expanding the career network for a job hunt after a career break. I couldn’t be happier!

How I believed in myself, studied hard, worked hard, never shied away from making mistakes and re-learning. I’m not embarrassed by making mistakes if I had to learn. In the end, everything worked out well. Got to be what I want to be. I kept the faith and did not lose hope. Emerged victoriously!

Conclusion

A good mentor once said:

“What someone could learn to accomplish is limitless, coming out of your comfort zone and overcoming your self-doubt is the key to make a comeback to the professional world”

I intend to follow this advice. My career journey has just begun. I plan on doing more projects to gain more experience and build a strong resume portfolio. I believe I will get a job soon! That’s my conviction! This is my story!

If this article motivates you to upgrade your skills to Machine Learning and AI, feel free to apply to real-world social projects here.