Machine learning and YOU in the Tech industry:
"When patterns are broken, new worlds emerge." - Tuli Kupferberg
The machine learning (ML) market was valued at $15.44 billion in 2021. By 2029, it's estimated to be valued at around $209.91 billion. Astronomical growth, won't you agree?
As you can clearly see, I've done my due diligence by typing in some keywords into Google and quoting the results as an authoritative entity. But besides this feigned bravado, what's the point of this?
There's never been a better time to transition careers into the ML space. Seriously. The best time was 10 years ago.
The second best time is now!
There are a few common misconceptions I'd like to tackle before getting to the various roles that might interest you as a practitioner.
Misconception #1: You need to have a PhD
The short answer is that you don't. The long answer is you don't for 99% of the roles. A Ph.D. in machine learning is needed IF and only IF you want to break into the top tech research labs as a research scientist. For just about any other role, you don't need one (unless you really want one and would like to be addressed as Dr. INSERT_YOUR_NAME_HERE).
The primary reason a Ph.D. is valuable in my opinion is that going through a rigorous Ph.D. program teaches you how to do systematic research, and how to formulate abstract ideas into concrete problems that can be solved.
I don't have a Ph.D., and I've transitioned from a systems engineer to a research engineer to a research scientist (humble brag, sorry). The reason I bring it up is that you need to know that this isn't a barrier to entry. You can and you should apply to roles that interest you, AND for which you have the skills to deliver on the job.
What you need is a curiosity itch, the ability to code, and the willingness to learn new things. These three skills will help you break into big tech, and grow in the machine learning space.
So, repeat after me - "I don't need a Ph.D. unless I absolutely want to go out and get one."
Misconception #2: You need to be really good at math
Machine learning is math-heavy. I agree. But, you don't need to know all the math in the world to get started. In fact for some roles in ML, you'll be so far removed from any kind of math that you'll wonder why you didn't transition sooner. I won't lie, there's some high school math that's really important. You can learn that on the job as and when you need it.
Don't let this stop you either. Please.
Misconception #3: Your past career experience is useless
One of the most beneficial skills to acquire in any kind of machine learning role is domain expertise. So whether you've been an accountant, or have worked in pharmaceuticals or any other field that seems remote to ML, know this - Your domain skills will actually help you, not hinder you.
Here's why.
The hardest skill to acquire is business intuition. Not ML. If you can't communicate why you need to use machine learning, or how it would solve your business problem, you won't get buy-in from all the stakeholders.
Your domain expertise will let you speak to non ML folks in a language they understand!
So treat your past experience as a superpower, not as wasted effort.
With these out of the way (read them aloud if you aren't convinced yet), let's look at the two broad categories of machine learning roles:
Machine Learning Research:
The goal of research is to push the boundaries of the field further and to solve fundamental problems. Essentially, the team may go, "Alright folks, let's see if this claim is valid or not and go down this rabbit hole". Some research may lead to products, BUT, that isn't why research is pursued.
Companies also have a flavor of research called applied research. This is usually directed to solving practical problems the company has, and may have shorter time frames.
Research roles are of two kinds:
Research Scientist: This is probably the only role in ML that usually requires a Ph.D. There are circumstances where a high performing engineer with a good track record of top-tier publications gets into this role, but those are far and few. A Research Scientist comes up with novel ideas, puts them to the test and publishes them. Additionally, they act as advisors to other engineers in the team.
Research Engineer: This role isn't present in all companies. Sometimes a company might use Research Engineer and Research Scientist interchangeably. The Research Engineer usually works in collaboration with the Scientists and runs experiments that evaluate ideas they come up with. This role doesn't require a Ph.D. or top-tier publications.
Machine Learning in Production:
As the name suggests, the goal of production is to come up with a product - be it a good or service. Machine learning in production usually deals with a need that the company's users have. Naturally, the constraints are different. In research, you might focus on beating the state of the art. In production, however, your goals may be ensuring that the ML system runs fast, reliably, and works well with the other parts of the product.
Thus there are more hats to don, and more opportunities to enter the field.
Below are a few different production roles I've seen in the industry:
Machine Learning Engineer (MLE): The MLE is a software engineer who has ML skills or can learn ML skills needed for the role. Their goal is to design, build and productionize ML models for business needs. In this day and age, this is one of the coolest roles to have. Your work directly impacts the product.
Data Scientist: A data scientist needs to form their own questions about the data and use machine learning or predictive modeling to answer them. Data science is a deep and rich field. As a data scientist, you'll turn data into business insights and help the organization make better decisions. The key phrase is unlocking meaning from data.
Data Engineer: They make it easier for others in the organization like Data Scientists, and MLEs to use data for their needs. To do this, a data engineer builds systems to collect, manage, organize and analyze data at scale. Raw data is hard for anyone to use, and in this age of big data, managing and creating an effective structure for the rest of the organization to use is an invaluable skill.
There are some newer roles emerging like ML ASIC Engineers, Infrastructure engineers and so on which might also interest you depending on your background.
Overall, there're a ton of roles that you can choose from depending on your skills and interests. You just need to start.
Key Takeaways:
You need just three things to get into the ML field:
A willingness to learn
The ability to code
Curiosity
There are many opportunities for you - Find the right starting point
Don't wait to learn everything and then start. Learn what you need. Learn the rest on the job.