So you want to get started in data science but aren't really sure exactly
what you want to be? A data scientist or a machine learning
engineer? Then you've come to the right place. Today we're going to talk about
five key differences I wish I knew before I got started. Welcome to 5
mins 5 ideas. I'm your host, Samanvay. Previously we
talked about five things I wish I knew before becoming a data scientist. Make
sure to watch that first before you move on to this video they'll help you
understand exactly what data science is all about. Let's talk about the
difference in roles between a data scientist and a machine learning
engineer. Data scientists are typically in charge of data driven problem solving
for business use cases. Most of their time will go
into research and development, engaging in proof-of-concept projects and they'll
also deal with stakeholders and customers externally and also within
the company. As opposed to this, machine learning engineers will
typically be responsible for taking proof of concept to
scale. Most machine learning engineers will also tend to have a skew towards
software engineering. It's important to note that you won't find machine
learning engineers in all organizations because it really
depends on what stage of the data science cycle a
company is in. If they're just starting off they might
just have data scientists that do everything end-to-end. It's only when a
company decides to invest heavily into data science
that it might need machine learning engineers to take their products
to scale. The second difference is in education. More often than not,
data scientists come from a variety of backgrounds. Most data scientists have
a stem background. They might have done maths, physics,
engineering or computer science as a degree at university whereas machine
learning engineers tend to be a little bit more skewed
towards software engineering. So more often
you'll definitely find software engineers that have made the transition
into machine learning engineer but it is uncommon to see people that have just
done a maths or physics degree in machine learning
engineering roles. The third difference is in the tech stack used by a data
scientist versus a machine learning engineer it's important to highlight
where data scientists and machine learning engineers sit on that data
science cycle. So initially any project will
usually be started by data scientists and they'll be conducting
a proof of concept to validate a certain approach. If they think that the idea's
got some legs they'll usually palm off the solution to machine learning
engineer who'll work to deploy it at scale. In terms of codings data
scientists primarily use python in tools such as jupyter notebook
which allows for rapid prototyping of ideas but it's not really best practice
if you want to deploy to scale now there's edge cases to this i know that
netflix does have tools that allow them to
deploy notebooks in port but that's definitely an
exception rather than the rule then if you look at machine learning engineers
they'll also be using python in combination with apache spark, scala,
golang, kubernetes to deploy solutions to scale. So the role
of a machine learning engineer is definitely more technically rigorous
when it comes to using software tools whereas a data scientist
is more adept at analyzing data. The fourth thing I wish I knew was the
difference in the mindset between a data scientist and a machine
learning engineer because data scientists are usually at the beginning
of the data science cycle they are more responsible for
exploration and research and development so they're more thinking on the model
level whereas machine learning engineers will
be more focused on the robustness of the code and the efficiency of it
additionally they can also be responsible for designing
labelling systems, model tests and also experimentation at scale.
The final difference is in the stakeholder management. Data Scientists
are typically the ones that deal with customers and
conduct presentations. If you value that customer time and that
interaction, I definitely recommend being a data
scientist because machine learning engineers on the other hand will
definitely sit a level deeper in the team so they'll be
more often than not interfacing with data scientists and possibly
product managers. That's 5 things I wish I knew before I got started in data
science. If you enjoyed the video be sure to like
and subscribe and I'll see you next time.