Advice From a Top 1% Machine Learning Engineer
Summary
TLDRIn this insightful discussion, Mita Bararia, a senior research scientist at Netflix, shares her journey to becoming an AI engineer and offers valuable advice for those interested in the field. She emphasizes the importance of foundational knowledge in mathematics and programming, the benefits of taking classes and building projects to gauge interest, and the potential for growth with a PhD. Mita also discusses the fast-paced nature of machine learning, the significance of soft skills, and her excitement for the future of AI in solving complex problems.
Takeaways
- 🎓 Pursuing a PhD or Masters in machine learning can provide a strong theoretical foundation and deepen one's understanding of the subject.
- 💡 Starting with the basics of mathematics and computer programming is crucial for building a strong intuition for machine learning concepts.
- 🚀 Hands-on experience through relevant jobs or projects is essential for applying theoretical knowledge in practical scenarios.
- 🌐 The field of machine learning is rapidly evolving, making continuous learning and staying updated with the latest advancements vital.
- 🧠 A strong foundation in fundamentals allows for easier adaptation to new methods and technologies as they emerge.
- 📈 Prioritizing technical skills is non-negotiable, but soft skills like communication, collaboration, and leadership are equally important for success in the industry.
- 🤖 The use of AI and machine learning is expected to expand, enabling software engineers to tackle more diverse and complex tasks.
- 🌟 The future of machine learning holds promise for high-quality innovation in areas such as healthcare and personalized recommendations.
- 📚 Reading seminal papers and revising mathematical concepts can provide a competitive edge in the fast-paced field of AI.
- 🤔 A personal choice between pursuing further education or entering the workforce depends on individual career goals and circumstances.
- 💼 The ability to make fast, informed decisions is crucial due to the rapid pace of innovation in the tech industry.
Q & A
What motivated Mita Bararia to transition from software engineering to machine learning?
-Mita Bararia was intrigued by the fields of mathematics and computing during her undergraduate studies in electrical engineering. This interest led her to pursue a job as a software engineer, where she further realized her desire to understand more about machine learning. After taking an introductory course on machine learning, her interest was solidified, prompting her to pursue a PhD in the field.
What advice does Mita have for someone interested in AI and unsure where to start?
-Mita suggests taking a class and building a project to determine if one enjoys the field. She emphasizes that understanding whether you like something is the best indicator of whether you should pursue it. She also highlights the importance of having a strong foundation in mathematics and revising these fundamentals as the field evolves.
Is a PhD necessary to become an AI or machine learning engineer?
-A PhD is not necessary to become an AI or machine learning engineer, especially for those transitioning from software engineering. There are plenty of resources available, such as online courses and seminal papers, to understand the field without formal graduate education. Gaining hands-on experience in a team setting can also help transition to a full-time machine learning role.
What are the benefits of pursuing a PhD, according to Mita?
-Pursuing a PhD provides an opportunity to focus solely on learning and growth. It allows for a deep dive into a subject and helps in maturing one's intuition about it. Additionally, it enhances written and verbal communication skills, builds confidence, and allows for collaboration on a global level.
How does Mita feel about the rapid pace of advancements in machine learning?
-Mita views the rapid pace of advancements as both exciting and challenging. She advises revising fundamentals and building intuition for basic models to keep up with the field's evolution. She also believes that with a strong foundation, one can adapt to new developments more easily.
What is Mita's perspective on the role of AI tools like ChatGPT in the future?
-Mita believes that AI tools will free up mental space by taking over tasks that can be solved algorithmically. This will allow humans to focus on being more creative and taking on tasks that require a human touch. She emphasizes that having a fundamental understanding is crucial, as AI tools are there to assist, not replace human knowledge and creativity.
How does Mita think the role of software engineers will evolve with AI?
-Mita believes that the role of software engineers will expand with AI. Engineers will be able to leverage AI for more tasks and achieve more through prompt engineering. She sees AI enabling more creative work and allowing engineers to take on tasks that were not possible in the past.
What are some soft skills that Mita believes are important for an AI engineer?
-Mita highlights the importance of communication, collaboration, decision-making, and being a pleasant colleague. She notes that these soft skills, in addition to technical expertise, can greatly contribute to success in the industry.
What excites Mita the most about the future of machine learning?
-Mita is excited about the impact that large language and foundational models can bring to various applications. She predicts that we will see high-quality innovations in areas like healthcare and search, where these models can be fine-tuned for specific tasks.
How does Mita view the importance of continuous learning in the field of AI?
-Mita emphasizes that continuous learning is essential in the fast-moving field of AI. She advises staying updated with the latest advancements by reading seminal papers and revising one's mathematical foundations to be prepared for future developments.
What is Mita's stance on the idea that it's too late to become a software engineer given the rise of AI?
-Mita strongly disagrees with the idea that it's too late to become a software engineer. She believes that the role of software engineers is not disappearing but rather expanding. AI will enable engineers to do more creative work and take on a wider variety of tasks.
Outlines
🤖 Introduction to AI Engineering and Career Advice
The video begins with the host addressing common questions about becoming an AI engineer and the nature of the work. The host introduces Mita Bararia, a senior research scientist at Netflix, who previously led the recommendation assistance team at Etsy. Mita shares her educational background, including her PhD in machine learning, and offers advice for those interested in AI. She emphasizes the importance of taking a class and building a project to determine if one enjoys the field. Mita also discusses the value of a PhD for those transitioning from software engineering, highlighting the theoretical foundation and the opportunity for focused learning and growth that graduate school provides.
🎓 The Decision Between Grad School and Industry Work
Mita discusses the personal choice between pursuing a PhD or working in the industry. She shares her personal experience of craving a pause in her career to engage in academic research, which she found beneficial for her growth as a machine learning scientist. Mita explains that a PhD program allows for deep dives into subjects and enhances collaboration and communication skills. She also addresses the concern that AI advancements are happening so fast that foundational knowledge may become outdated, suggesting that revisiting basics and understanding seminal papers is key to staying relevant.
💡 Balancing Technical Expertise with Soft Skills
The conversation shifts to the importance of soft skills in addition to technical expertise for AI engineers. Mita stresses that while technical skills are fundamental, soft skills like communication, collaboration, and decision-making significantly contribute to success in the industry. She also mentions the value of being a pleasant colleague, as it fosters a positive work environment. Mita then discusses the rapid pace of innovation in AI and machine learning, expressing excitement about the potential of large language and foundational models to transform various applications. She predicts that these models will enable high-quality innovation in areas like healthcare and personalized recommendations.
Mindmap
Keywords
💡AI engineer
💡Machine learning
💡PhD
💡Electrical engineering
💡Software engineer
💡Fundamentals
💡Graduate school
💡Innovation
💡Communication skills
💡Soft skills
Highlights
Mita Bararia, a senior research scientist at Netflix, shares her journey of becoming an AI engineer.
Mita's transition from electrical engineering to software engineering sparked her interest in machine learning.
The importance of taking a class and building projects to determine interest in a field.
It is possible to enter the AI field without a PhD through online resources and hands-on experience.
A PhD provides a theoretical foundation and time to mature one's intuition about a subject.
The fast-moving nature of machine learning, with advancements being made almost daily.
The advice to revise fundamentals like mathematics and linear algebra for a strong basis in machine learning.
Mita's experience in college without smartphones did not hinder her ability to learn about mobile technology later.
The personal choice of whether to pursue a PhD or work, depending on one's career stage and desires.
Collaboration and communication skills are vital for a successful career in machine learning.
The value of writing papers during a PhD to hone written and verbal communication skills.
The importance of understanding foundational concepts rather than just memorizing information.
The prediction that large language and foundational models will lead to high-quality innovations in various applications.
The potential for AI to enable more creative work and take on more tasks in software engineering.
The advice for individuals to continue learning and adapting as the field of AI evolves.
The impact of AI on healthcare and recommendation systems through fine-tuning large models.
The significance of being a good colleague and the value of soft skills in the tech industry.
Transcripts
I've been getting a lot of questions
about how do you become an AI engineer
what is it like to work as an AI
engineer so today I thought I am going
to invite one of the best machine
learning Engineers working at a big tech
company here Mita and she's going to
give us advice and tell us more about
how she became an AI engineer hopefully
so you guys can also learn and become an
AI engineer too so Mita thank you for
being here tell us a little bit about
yourself hey everyone I'm Mita bararia
I'm a senior resarch scientist Netflix
prior to Netflix I was working in Etsy a
two-sided marketplace where I was Tech
leading the recommend assistance team
I've have done my PhD with
specialization on machine learning
that's really impressive just the person
that we need to talk to because we get
so many questions on YouTube about
people who are interested in Ai and what
is this new thing and how do we do it
you started very early on before AI
really picked up so yeah tell us the
secret like what sport do your interest
in getting
I did my undergrad in electrical
engineering I love some of the subjects
of electrical engineering but then
towards the end of my undergrad I
realized that I love ma mathematics and
Computing and programming more I uh
decided to take up a job as a software
engineer after mandag grad while I was
enjoying pure software engineering I
also had this itch of knowing more about
a field that's when I chose to come for
my masters in the first semester I took
a course on machine learning
introduction to machine learning in the
mathematics Department Department wow
really sparked my interest and I wanted
to know more about it and that's when I
decided to go for my PhD in machine
learning I really like that because one
of the advice that I always give to
students or people who are exploring is
take a class you build project see if
you even like it maybe you do maybe you
don't and that's the best indicator
whether or not you should pursue it
absolutely you figure out whether you're
into it because you can only be really
good if you're really into it exactly if
you absolutely hate it like don't
it doesn't matter how popular it is
absolutely you did your PhD program
that's another question that we get a
lot do you need a PhD in order to get
into AI machine learning be a ml
engineer or AI engineer if you're
transitioning from a bu software
engineering I think there are plenty of
available resources whether it's courses
and Cora or other online platforms
whether is reading some of the seminal
papers and understanding the
fundamentals possible to understand the
field without actually going to a formal
graduate school right and then F get
into a team which gives you an exposure
in getting hands-on experience then you
can slowly be a full-time ml engineer
from a p software engineering so I think
it's definitely possible somewhat to be
successful uh you need to like some of
the uh theoretical Foundation which I
feel a PhD or grad school gives you that
BS to be able to spend that time with
the subject so that you mature your
intuition about the subject and it gets
hard when we are learning that a job
because we also delivering at a much
faster base if you want the time and
space to be able to really appreciate
the the study and the learning it is
nice to be able to go to graduate school
or PhD program where you can just focus
on learning and growth exactly because
when you're working full-time you just
don't have much time it's a luxury to be
able to study right absolutely yes if
you're not in a place that where you can
go to school or if you're not a school
type of person there are still ways to
get into it by finding opportunities
within where you're already at
absolutely yeah one of the advice I do
give to folks who are getting into ml
revise your fundamentals like
mathematics or the basics of linear
algebra it really helps to build on top
of that ML and eii as you know is
extremely fast moving as a field like
last year in one of the big conference
in machine learning kdd one of the kot
speaker said seeing advancement per day
the way we used to see in a year so the
amount of that need to come out in a
year is now being published every day
almost so it's really fast moving I got
my PhD most of the deep learning methods
that we used today did not even exist
how you like it up is if your
fundamentals are very strong so you just
like back and revise those fundamentals
build intuition for the basic models
like learn logistic regression really
well and explain deepal network from it
because a lot of fundamentals are very
translatable and then you build on it
and then when we have large language
model generative AI foundational model
you can build up on your fundamentals so
if you have the time read up some of the
seminal papers read up your revise your
math and then you're set for the future
like Basics are clear then as the field
is moving you're able to catch up I love
that a lot I also used the example like
I was in college we didn't have
smartphones I know I didn't know
anything about smartphones but I got a
job in Mobile and I was able to learn it
because once you have the basic
fundamental is much easier to pick up
new things one of the biggest dilemas
for a lot of students and we get this
question a lot should I go to grad
school or should I get a job how do you
think going to a PhD program serves you
now as a machine learning or AI engineer
working in the industry the decision of
going pursuing a PhD degree or even grad
school if you're getting your Masters I
think it's definitely a very personal
choice uh and it really depends on where
you are at at that point in your career
if you're already a ml engineer with
your undergrad degree and you're able to
perform really well you don't feel the
need to pause the working and go back to
school and revise and learn the subject
further for me personally I was craving
that pause and being a part of Academic
Program where I can just purely pursue
knowledge that really helped me in my
trajectory as a ml scientist now in
Industry because I could really spend a
lot of time with the subject you spend
many many hours with yourself you think
about one thesis question and spend many
hours days and months and years to to
find the right answer I enjoyed that
Journey my pH was very collaborative I
worked on building machine learning
models for disease prediction and R
stratification so it taught me a lot of
collaboration communication skills while
I'm picking up a lot of deep technical
expertise it also helped me become much
more confident in my ability because of
the individualistic of the pursuit
another skill set that I think PhD
program can really give you is we write
a lot of papers as a PhD student it
helps really really hone your written
communication and verbal communication
and that's one skill set that is
absolutely invaluable in Industry
getting those chances to really horn
your technical skills while developing
communication while developing
confidence within yourself learning to
compete at a very Global level that
builds up a confidence that builds up
your belief in your own ability
yesterday or a couple days ago I saw
this comment on one of my videos this
person was like now chbt can read all
the papers for you so you don't need to
learn how to read did and I thought well
that's not like saying we have
calculators so you don't need to learn
yeah and if Char is reading and
understanding that what are we going to
train CH on in the future yeah right we
don't want just machine generat text we
want real people to be still continuing
to able to write journals and write
papers and the human J text is still
going going to be around and I feel like
even if you do use chbt is just a tool
to help you if you don't have the basic
fundamental knowledge it's not that
helpful even if cha PT reads it for you
Chach PT is the one learning not you
that yes I think in the future we will
free up our mental space we have already
feed up our mental space from memorizing
right we use Google we use other
information on internet we don't
remember everything we needed we used to
with chat GPD with gbd4 with these
generative models I think we will free
up even more of our mental space with
the things that can be solved as a
result as humankind I feel we'll be more
creative eventually it might take a few
years maybe a generation to get there
every other pivotal moments uh like
Industrial Revolution and uh when
internet happened there was unsettlement
in humankind But ultimately we Le we
ended up in a better place yeah I feel
like as humans we are going to get more
creative we're going to focus on other
kind of energy that these tools and in a
way you rightly put so that these are
tools that will help us be more
productive but at the same time we will
as humankind start using our brain power
for something else absolutely I I 100% %
agree I read this book recently called
range I think he said something about
how human IQ has been increasing every
decade as an overall human species we're
getting smarter counter argument I hear
often is like how many phone numbers do
you do you know I'm like why do
we I remember contextual things a lot if
you tell me something that happened to
you if you teach me a concept I'll
remember it forever but if you tell me a
name I'll forget it here growing up I
would hear uh folks say senior previous
generation say that oh this person is
super smart because he or she can
remember stuff I was like why is that
equated to Smart yeah good memory is
part it's part of it but contextual
memory or understanding concept it's
hard to quantify that exactly that's why
I think it's easier to just say if
someone remembers a lot of stuff it's
smart versus it's hard to quantify when
you understand a concept or you're very
creative now I think I also read about
how back in days when the writing first
came out people would say oh my gosh no
one has to remember anything anymore now
we're going to get all done these tools
are serving us and allowing us to be
focusing our energies on more creative
things but maybe in the generation or
two they would think oh back in the days
people didn't do this 100% totally I
think we're very aligned on this and
another question that we often get a lot
is how important is it to balance your
technical expertise with other sof
skills like communication and Leadership
skills to perform your job well of
course the technical skills is the
foundation right you need the expertise
that you are required to perform uh at
any given job so technical skills are
non-negotiable but beyond that a lot of
the soft skills I think go a really long
way communication and collaboration I
think at various point we spoke about
how important it is let's say two
individuals who have equal amount of
technical expertise but one person has
ability or has horned the ability to to
communicate succinctly and and
articulate things and in a timely manner
can go a longer way compared to the
other person so technical communication
to be effective collaborator to be
effective uh researcher to be succinct
and effective Communicator itself is a
very very valuable skill to be
successful in industry in addition to
that other soft skills where just
genuinely being a good colleague to work
with person who others are delighted to
actually work with I think also helps
because who wouldn't want to work with
someone who is who is a happy person to
work with compared to who is a grumpy
person to work with so those are kind of
almost non-quantifiable skill set that I
think are good things to think about
actively and have the to mind uh beyond
your technical skills communication
being a collaborator fast decision
making is another soft skill that I feel
is important because Innovation velocity
in any company in industry is fast
especially machine learning AI the field
is moving fast so you want to see
through the all possible options but
quickly enough and then commit to
something and move on rather than taking
a really long time to get to the
decision of what we should even build
all this thing kind of come together
communication decision making
collaboration and just being a nice
person to work with and you mentioned
several times how things are changing
really quickly what are some things that
we should look out for and what are you
excited about as an ml engineer there's
tons of innovation happening at a pace
that we have never seen before overall I
am very excited about uh the impact
these large language model and the
foundation models can bring to different
applications so what I mean by that is
traditionally we would build specific
models to solve specific problems now
with a really large models foundational
models it's almost like we're able to
train a model to be fifth grader or
undergrad level student and then for a
specific task we can fine-tune the model
to S to be a PhD in a specific area
right so that's how I think about it
instead of training a model from scratch
now we have this well understood about
the basics of the world kind of models
which then we can and for the train to
make them extremely well in specific
task my prediction is that soon we'll
start seeing high quality Innovation
happening for example Healthcare
problems or even recommendation and
search where we find tune the model
Leverage The Power of these greed models
but also solve very specific task and do
it really really well proprietary data
will become even more powerful if you
have a very specific data for a specific
task like gene expression data or
Healthcare data then you can Leverage
The Power of these large models that are
trained on very big uh data set and then
use your propriety data to train
fine-tune the model and get really high
performance we are entering into this
era where we'll see much more improved
applications of ml for very impactful
task sounds like you're more excited
about expanded ability for us to build
more and achieve more as sofware
Engineers yeah yeah and leverage AI for
many more tasks that we have not been
able to leverage that reminds me people
also ask is it too late to become a
Sofer engine should we not do it anymore
more is it useless I we have the answer
yeah yeah absolutely no no it's not
useless I mean the kind of software
engineering you will do is different we
are evolving we we will we are requiring
more and more let's say prompt
engineering the skills that require uh
will expand I don't see it's
disappearing it's expanding yeah like
kind of like what you said earlier about
it will enable us to do more creative
work and take on more tasks and maybe do
things that we haven't been able to do
in the past yes well thank you for
sharing your advice thank you so much
for having me yeah and thanks everyone
for watching too thank you thank you
byee
Посмотреть больше похожих видео
Side projects to break into Product Management!
Töissä ICT-alalla Tampereella: Pauliina Mäkilä, vanhempi liiketoiminta-analyytikko
Nursing Informatics | An Inside Look
A Day in the Life of a Project Manager | Indeed
Truth in Data Science | Jaya Tripathi | TEDxYouth@BHS
How did she crack Meta Software Engineer Role | London🇦🇺 | Garima Rajput
5.0 / 5 (0 votes)