In this lesson, we are going to talk about the selection of a stack of tools, uh, when building an
LMS application before that, just very quickly, a, I want to tell you that in the attached materials,
you are going to find a document talking about one of the alternative LMS to ChatGPT, which is palm
A.
I think that is a little bit early to, uh, to show this information for you, but we just wanted to
a tell you about that.
Of course, there are alternatives to ChatGPT.
And these alternatives are, uh, becoming more and more, uh, strong.
So our prediction is that in 2024 and the coming years, uh, we are going to have more and more alternatives
to ChatGPT in the open source and also in the private, uh, field.
So just just a quick note about this document you have in the attached materials.
Now in this lesson, we are going to talk about the selection of, um, the stack of tools.
You have also a document, uh, about that in the attached materials.
And the interesting thing.
About this.
Is that a you will see that.
Some of the tools that are most popular among the artificial intelligence engineers.
I'm not going to be the tools we recommend you because a in some cases these, uh, um, artificial
intelligence engineers responding the, the, the 2023 survey, they are working in very, very big
companies.
So these are engineers all over the world that are coming to an international summit in the city of
San Francisco for one whole week.
So, as you can imagine, these kind of professionals usually come from big multinationals.
And because of that, they are going to use very large tools.
And in some cases, the reason for using these large tools are that they are, you know, inside very
big IT teams that are using other, you know, compatible frameworks, etc..
So our recommendations are going to be based not just in the popularity of the tool, but in the relationship,
you know, cost benefit of the tool.
So we are not a sponsor by any company or any brand like any other, like some other courses you can
find there, they are sponsored by some tools and they are recommending those tools.
It's not the case of this program.
We are going to make honest recommendations based on our own experience.
So in the case of the stack used when the artificial intelligence engineers responded the 2023 survey,
they highlighted some, uh, tools.
So.
In the orchestration framework, for example, they responded a that long chain and lambda index are
practically tied.
This is not the reality we are seeing, uh, in the community from our point of view.
We see that long chain is still far more popular than Lambda index, uh, in the community of users.
But the more you go to the professional level user, the more you see lambda index, uh, increasing
in in use.
So it is a reasonable to see that in this survey, uh, from professional artificial intelligence engineers,
especially engineers that are working in big companies launching an lambda index are practically tied.
As the orchestration framework most used.
So this is interesting because if you extrapolate this survey to the general public, probably land
chain is going to be by far the most used.
But when you go to the professional, uh, segment, you see that lambda index is more used.
We will see why, uh, in a, in a, in a future, uh, lesson.
In the orchestration frameworks you would see, you will see that there are some other frameworks,
uh, highlighted as well in the survey.
In the database you will see a lot of a different alternatives, uh, highlighted in the survey.
You will see that pine cone is the top one.
Postgres the second one, then comma, supabase, redis, etc. etc..
In our case you will see that in the program we are going to make use of Postgres, comma, a face and
some others as well.
In the monitoring and observability frameworks.
You will see that in the survey.
We have also a large number of frameworks, uh, uh, highlighted by the by the artificial intelligence
engineers that responded this uh 2023 survey.
In our case, we will highlight Lang Smith and we will explain why, uh, in in the next lesson, a
for model serving and hosting uh, tools.
You will.
See also a large number of, uh, alternatives highlighted in the survey.
This is something that you are going to see, uh, every time you are studying a what is happening in
a very early stage field like the one we are, uh, we are, uh, in the LM applications field is still
in a very early stage.
So you have a lot of, uh, tools, a lot of frameworks that are trying to position themselves as leaders.
But it they are not there yet.
Right?
So we have a lot of competition, a lot of alternatives, but we still don't have a solid, uh, positioning
of tools.
So in the model serving and hosting, you have a lot of them.
In my opinion, the most interesting is OpenAI directly.
But you, you have, you know, alternatives like hugging face like replicate, for example, like a
Amazon Web Services SageMaker, Google Vertex as well a Azure.
Uh, but in my opinion, the most interesting and the most use for the general public is going to be
OpenAI directly, especially in 2023.
Let's see what happens in the in the future for model training and fine tuning.
This is a category included in the service in the survey.
I don't think it is very relevant in the real world for companies mid-size, small size, startup,
etc. because fine tuning is something very expensive and very complex.
And you will see that most, uh, AI engineers are not using fine tuning, are using LM applications
or other things like prompt management.
You have some, uh, tools highlighted here.
Not very important in this in this moment for us.
So I just wanted to show you in these lessons, in this lesson, what are the most popular tools you
highlighted by professional artificial intelligence engineers?
And to tell you that in some cases, the most popular are not the ones we are going to recommend.
We are going to make recommendations based on cost benefit relationship, based on our own experience
and the tools we think are going to be more interesting for you from a cost point of view, from an
efficiency point of view, etc..
In the next lesson, we are going to talk a little bit more about the selection of the orchestration
framework.