- Hi.
U.S. Bureau of Labor Statistics
in late 2017 reported
that the rise of data science
will create additional
10 million jobs by 2026.
Now what this has
resulted is every college
and academics introducing
machine learning
and artificial intelligence
as part of their curriculum.
Not only colleges,
but also there a lot
of open, online courses
and institutes have been offering
machine learning as part
of their curriculum.
In fact, there's even an MBA
in Artificial Intelligence.
You can Google it.
And you can search it.
Now it's safe to assume that all this 99%
of the courses that are out there today
is focused purely on machine learning.
Now what is the problem with that?
Now when you talk about data science
there are two aspects.
Not all data science
requires machine learning.
The second part is even
in data science project
that requires machine learning,
machine learning constitutes only 20-30%
of the entire life cycle.
Now data science is a process that takes
in undefined raw data to generate insight
and communicate the results
that can be actioned on.
There are multiple roles
that come into play
in a data science life cycle.
It can be a product owner.
It can be a business analyst.
It can be a data analyst.
It can be a domain expert.
It can be a data engineer.
And then you have the machine engineer
or the machine learning engineer.
The machine learning engineer can be
an deep learning engineer as well.
And post that you need
visualization experts
to communicate the result
or show dashboard on
the current state of health.
And then finally your software engineers
who work with the data science team,
the machine learning engineers,
and data engineers
to operationalize the
models into production.
So there is an whole bunch of role
that come into play
in entire data science lifecycle.
Now when we say there's 10 million jobs
will be created by 2026,
as per U.S. Bureau of Labor Statistics,
the job that are created is
not only for ML alone.
It's a combination of these entire roles
I have been talking about.
They all constitute to
this 10 million jobs.
Now today what I see
most of the college grads
who are gravitating out of the college are
completing moving towards
learning machine learning.
And, in fact, that's what the courses
have to offer as well.
Now demand for ML engineer
alone will be very less
compared to the supply out there.
And that's going to be
very true in geographies
like Asia and Africa.
Now U.S., Europe, and Australia
may have an supply and demand match.
The point I'm trying to make over here is
one can also look
outside of ML engineering
when they're trying to base their career
on data science.
There are multiple aspects
and multiple roles that come into play.
Don't be completely
focused on ML engineering.
ML engineers, the job looks good.
The job looks jazzy.
Playing with algorithms is interesting.
But at the same time be job ready,
and be industry ready as well.
Evaluate your skill set
and see how close you map to that.
Say you are in the data base side,
or you have been in the
traditional ETL world.
It's easy for you to move on to the
data engineering side of it.
In fact, for every
machine learning project
there is an one to four
data engineers required
depending on the complexity of the project
with the ratio of data
scientists who are there.
Right?
And, similarly, if you are
from a traditional web development
or a software engineering background,
you can learn about
cloud-native technologies
and also the AI platform that supports it.
That will you migrating towards the
data science project
and deploying the models out there.
There's one more aspect of it.
AutoML is getting real day by day.
Now AutoML cannot completely
take out a ML engineer,
but today at least 50% of the activity
that ML engineer does,
AutoML can do it.
And over time AutoML,
it may reach up to 70%.
Not more than that.
But this will also reduce the demand
for ML engineer overall in the project
because some part of task can be automated
using AutoML tool.
So choose your career wisely.
And choose your stream in the data science
process that you want to,
to study.
And that you want to be
market ready and job ready.
Thank you.
