In this lesson, we are going to talk about the difference between the data scientist or the machine
learning engineer and the generative AI engineer, the new generative AI engineer, or just AI engineer
for short.
We are recording this video on July 2024, almost one year after we started working in our bootcamp.
So I think this is going to be a very interesting update.
We are going to talk about our personal journey and some anecdotes regarding this topic.
We will we will remind our position since day one.
And we will explain what has been the journey for corporations and recruiters in the job market.
We are going to see the differences between traditional AI versus generative AI process today.
And we will review the five key differences between the data scientist or the machine learning engineer
and the new generative AI engineer.
So I began my professional journey by studying data science, machine learning, and deep learning.
My main interest since the beginning was generative AI applications, but at that time you wanted to
learn about artificial intelligence.
There wasn't anything else than data science, machine learning, and deep learning courses.
So afterward I delved into generative AI engineering and the development of large language model applications.
I studied everything that was out there, in most cases a no formal education, no formal programs,
but different pieces that together made or started making the generative AI engineering journey.
In this journey, in this personal journey, I.
I met a lot of data scientists and machine learning engineers who mistakenly believed they mastered
generative AI and who mistakenly thought that they were, like, entitled to be the next generative
AI engineers.
And you will see, this is not, uh, very logic or definitive conclusion, as you will see in my own
journey.
Uh, I have been, uh, well, my own journey inspired me to create the first online bootcamp dedicated
to generative AI and LLM app development worldwide, and you are seeing right now the top online bootcamp
worldwide in this area.
Our position since day one regarding this issue, the traditional AI engineers versus the new AI engineers
has always been the same.
From the beginning, we have emphasized that generative AI is distinct from traditional AI, which includes
data science and machine learning.
Engineer.
You have different skills.
You have very different mindset, as we will see.
And.
the skills and the mindset require for a generative AI engineer are significantly different from those
needed for a data scientist or a machine learning engineer.
What we have also learned is that you have many possible ways to become a generative AI engineer.
You can start from scratch and this program is designed for that.
You have any experience at all in software, in artificial intelligence, in machine learning, you
can follow this bootcamp and you will become a generative AI engineer.
Second, you have the one of the most popular a uh, ways we have seen, uh, in this bootcamp is the
data scientist or machine learning engineer that wants to become a generative AI engineer.
So this bootcamp is like a this transition bridge for them.
We have also seen a lot of software developers, back end software developers, front end software developers,
and also full stack software developers that become generative AI engineers through this bootcamp.
So there are many possible ways to become a generative AI engineer.
Data scientist was once hailed as the sexiest job of the 21st century.
However, in recent years, a new contender has emerged the AI engineer.
Right now, you are studying a bootcamp that is going to put you in the most sexy job position you can
imagine for the next ten, 20, 30 years at least.
For companies and recruiters.
The change has been very important.
Initially, there was considerable confusion within corporations and among recruiters.
Many of them assume that generative AI was a domain suitable for data scientists or machine learning
engineers.
So it was very interesting one year ago to see generative AI departments in companies, in big companies,
and they assume that this was a responsibility naturally taken by the traditional machine learning engineers
or data scientists.
And that was wrong.
The same thing happened with recruiters when we started looking at the initial job offers for AI engineers,
we saw a lot of extremely confused job descriptions.
So they were they were mixing, you know, the requirements of the traditional artificial intelligence
engineer with the generative AI engineer, which are very different.
We are going to see that that.
So this was one year ago, one year and a half ago.
Over time, however, there has been a a growing recognition of the unique skills and profiles necessary
for generative AI.
So now we are seeing a progressive realization from companies and from recruiters of the new reality.
We are still, though, in a transition period, so we are still seeing many companies and many recruiters
who do not understand the differences between these two profiles.
And you will see we have a lot of difference.
So let's talk very briefly about the traditional machine learning process or traditional artificial
intelligence process or workflow, and the new generative AI process.
Here you have in the screen a way to summarize these two different processes.
In the traditional AI process, you usually start with your repository of data.
You then use an analysis framework to gain insights about this data, and then you build an application
to transform these insights into actions.
Okay.
And finally you implement a feedback loop.
A a feedback loop to apply the lessons learned to your data repository.
So this is a continuous cycle okay.
The generative AI process is very different.
We start with what we can call universal data.
So our LM models our LM foundation Foundation models, they have like all the knowledge in the world,
you know, it's not exactly like that, but it's just a simplification.
So then we use Llms to interact with this universal data.
And remember this part of the process is outside your organization.
Then you use prompting and tuning with your data repository.
You use all the generative AI engineering techniques and all the data management techniques for generative
AI.
And we, as you know, talk a lot about those in this, uh, in this bootcamp.
Right?
So we are talking about, uh, platforms or frameworks like launching Landgraf, uh, crew, AI, uh,
Lang Smith, etc., etc., etc. we have a lot of tools and we have our own data that now is a playing
a role with the LM and the universal data because as you know, these LMS and these universal data are
not your company's data or the data you are interested in, right?
So you need to include that in the third step.
Next, you build an application to transform insights into action.
And then you implement a feedback loop to apply lessons learned to your data repository.
These skills are totally different from these skills.
We are going to see in the following slides the huge amount of differences we have between these two
processes.
So if we need to summarize the five key areas where data scientist jobs differ from AI engineer jobs,
and when I say that data scientist, I'm also referring to machine learning engineer.
As you very well know, these are not exact uh, positions or data scientist is more analytic and machine
learning is more predictive.
But we for the sake of this presentation, we are going to put them in the same field which we can call
traditional artificial intelligence.
Okay.
So these are the five key differences.
We are going to talk about traditional artificial intelligence and the new artificial intelligence or
generative AI.
I have different type of projects, different type of data used, different models, different workflow
and different roles.
And when we are talking about workflow, we are talking about tools, skills, processes, etc...
Right?
So they are very different.
You can say they are two different careers.
They are two different job paths.
Let's talk a little bit about the type of projects.
So a data scientist or a machine learning engineer is essentially what we can call a data storyteller.
They handle massive amounts of messy real world data.
They use mathematical models to translate data into insights, and they employ things like descriptive
analysis analytics to describe the past, like for example, what we can call exploratory data analysis,
clustering, etc...
They also use what is called the predictive predictive analytics to forecast the future, for example.
They use regression models for value prediction for example, to guess the probable price of a house,
or if some operation is going to be a failure of or a success, etc...
Right?
They also use classification models, uh, for differentiating between different possible outcomes,
etc...
Right.
So the type of projects are data scientists or a machine learning engineer are working on are descriptive
analytics and predictive analytics.
So they are data storytellers, and they also use some kind of predictive analytics as well.
But a generative AI engineer on the other hand, is more like a system builder is what we can call a
generative AI system builder or app builder.
In my opinion, app is a CLI is a more clear expression than system, but you are going to see both
expressions a we generative AI engineers build applications.
We build systems.
So we use foundational LLM models to build generative AI applications that transform business processes,
not just business processes.
We do we build applications that can do all kinds of things.
And this is just the beginning.
Generative AI applications are going to be everywhere.
So generative AI engineers handle what we can call prescriptive use cases to choose the best course
of action for, for example, applications for decision optimization for recommendation engines, etc.
all that can be called prescriptive use cases.
And we also handle what we can call generative use cases.
And this probably is the most important area right now I'm talking about artificial intelligence agents
chatbots for conversational search through information retrieval like for example, RAC, etc...
Right?
So as you can see, the types of projects are totally different.
Here we have a.
Descriptive analytics data storyteller and predictive analytics.
I predict the price etc...
What generative AI engineers are doing is totally different.
We are building applications that can do all kinds of things.
This is a huge universe.
The the I mean, the amount of things we as generative AI engineers can do are enormous, and the difference
between the limited range of projects that are data scientists or a machine learning engineer can do
is very important.
So type of projects one main a key difference.
Second type of data.
So if you think about the type of data that the data scientist or the machine learning engineer is used
is using, you will see that most frequently a they use a structured data, what we call tabular data
tables.
Right.
With hundreds or thousands of observations that they usually need.
Cleaning is what we call data preparation.
So we have these huge databases or these huge spreadsheets.
And we first need to clean this data, right?
As a machine learning engineer or a data scientist, This clean data is then used to train various machine
learning models.
So most of it, most of all the data scientists and the machine learning engineer is going to work with
structured data, with tabular data spreadsheets or data sheets or a databases.
Excuse me.
What about the generative AI engineer?
Well, the most frequently used data is what we call unstructured.
So here we are working with text.
We are working with images.
We are working with videos.
We are working with audio files.
And the most common model is an LM, which requires billions to trillions of text tokens for training.
So you are starting to see the difference of models as well.
And you will see this is going to have repercussions in computer power, for example, training time,
etc., etc. but here you see the second key difference.
Type of data.
What about models?
The data scientist or Machine Learning Engineer toolbox contains hundreds of models and algorithms.
Each use case requires gathering a different data set, data set, and training a specific model which
is often narrow in scope, as smaller in size, and requires less computational power and training time.
The generative AI engineer has a very different toolbox.
They typically include one type of model, what we call the foundation model.
And this foundation models they can perform a wide range of tasks, are very large in terms of parameters
and require substantial computational power and training time.
Okay.
So models are very different.
The data scientists and the machine learning engineers, they work with the small machine learning models,
which are specialized, use small number of parameters, less compute power, and less time to train,
usually from seconds to a few hours.
But in the case of generative AI engineers, we are only using one kind of model, which is what we
call the foundation model.
These models are huge, but are also super versatile.
They are not doing just one thing.
They can do millions of different things.
They use a higher number of parameters, a higher computer power.
And of course they require a higher time to train from weeks to months.
Okay.
So very different models.
What about the workflow.
So the typical data scientist or machine learning engineer is working in a process that involves first
gathering data.
Second, preparing this data with cleaning, feature engineering, cross validation, etc. then use
this data to train models.
And finally deploying the model, usually in the cloud for real time prediction or whatever you are
going to do, right?
So the workflow of the machine learning engineer is very, very, uh, structured.
What about the generative AI engineer workflow or process?
Well, we start by working with pre-trained models.
We are right now living what we can call the era of AI democratization, because right now, a all these
foundation models are widely accessible, right?
We then use natural language instructions to prompt models is what we call prompt engineering.
And and careful because as we said in the latest Proam engineering uh, advance uh, presentation prompt
engineering is not just about wording.
Prompt engineering is about communicating with foundation models.
And that includes a lot of generative AI engineering, use of different frameworks, techniques, etc.
so well take a look at the last presentation.
We include the last video we include about advanced prop engineering, because probably you are going
to see a difference between your initial conception or your initial assumptions about prop engineering
and what is really prompting engineering today.
So again, we as generative AI engineers work with pre-trained foundation models using natural language
instructions to prompt them to, to communicate to, to to give instructions to to these models.
And we employ frameworks to build large generative AI applications.
So in this bootcamp, you are going to see most of these generative AI frameworks and how we work with
them in order to build generative AI applications or what other people call generative AI systems.
And finally, in many cases, the the generative AI engineer is going to embed these applications into
larger workflows or larger systems that have a user interface, automation, etc., etc...
Okay, so the workflow of the data scientist or a machine learning engineer and the workflow we use
as generative AI engineers is totally different.
It's not just the workflow, it's the tools is the techniques.
What about the role?
And in my opinion, this is a very, very important key difference.
So what is the the role that a data scientist or a machine learning engineer is playing.
What is the the personality?
What is the mindset?
What is the attitude of these professionals?
Well, they are mostly analysts, mostly story tellers.
They mostly work with statistics, graphs, material, machine learning models.
And they they mostly provide insights or tools for other people to take action.
But the generative AI engineer is a total, totally different kind of animal.
We are builders.
Our role is more similar to the role of the full stack developer.
We are generative AI developers, but also part back end developers and part front end developers ourselves.
Okay, so data scientists and machine learning engineers are analysts and storytellers working with
statistics, graphs and machine learning models.
But generative AI engineers are builders and they are more like full stack developers handling generative
AI development, back end development, and front end developers and front end developers.
So the generative AI engineer is much more like the traditional full stack web developer.
We can create so many different kinds of projects.
A we can do so many different things.
We play a an absolute, a integral role in all these processes.
We are not just providing insights or working with statistics or working with graphs.
No no, no.
We are building things.
We are building products.
We are building applications.
We are building systems.
We are changing the world.
So for me this is the most important key difference.
So with a traditional artificial intelligence, with data science, with a machine learning engineering
U.
It was it was boring.
It was just playing with numbers and graphs.
But you you really don't play a significant role into making revolutionary new things, into changing
the world.
But as a generative AI engineer.
Yes.
Now, the possibilities you have in your hands are enormous.
You can work as an entrepreneur.
You can have your own startup.
You can lead a development, a department.
You can work in every kind of industry you can imagine.
You can be a consultant.
The sky is the limit for you.
It's a total different situation.
Okay, so the important thing, uh, just to, to end this, uh, video is to say that we are still
in a transition moment.
So there is still, for example, there is still overlap between data scientists and machine learning
engineers and generative AI engineers.
In many companies there is still confusion.
Despite the differences, there is still this, uh, overlap.
Data scientists may work on generative AI projects, and generative AI engineers may handle traditional
data science tasks.
Both fields are evolving very rapidly, and the distinctions between the roles of data scientists and
generative AI engineers continue to become more pronounced.
So in my opinion, this lesson is very important to clear some misconceptions.
Some of you may still have because even with our students, we sometimes find these, uh, confusion,
uh, of data scientists or machine learning engineers that still do not understand the main differences,
uh, people from the business world, people from the the human resources world, newcomers that they
do not understand.
The main differences between what we call the traditional artificial intelligence and the new artificial
intelligence.
So in summary, my last comment is that you are in the most exciting field right now all over the world,
so enjoy it.