So guys, we are going to continue the generative AI on cloud series.
And in this video I'm going to probably discuss about the gen AI project lifecycle.
Now, since you already know that, we are definitely going to develop a lot of applications specifically
in cloud from the basic data ingestion till the deployment.
So it is very much necessary that you actually understand a generic workflow of the gen AI project lifecycle.
So let me quickly go ahead and let me go ahead and write some amazing things for you in this particular
notebook, and I will be explaining you completely, step by step, how you can probably see or follow
a project life cycle.
And uh, as we go ahead, there will be a lot many things that will be coming, like LM ops platform.
Uh, and uh, we will be working specifically with Azure, uh, AI studio, AWS SageMaker studio and
all.
So definitely both the clouds will get covered.
So before I go ahead, please make sure that you keep the like target of all this kind of videos till
1000.
That will definitely motivate me.
And I've been exploring many more things so that you get the right kind of guidance and knowledge.
So let me go ahead and let me start the gen AI project lifecycle.
Uh, with respect to this gen AI project life cycle, I would like to make this entire life cycle into
4 to 5 steps.
Okay.
The first step, that is nothing.
But here I'm just going to write over here it is basically defining the use case okay.
So what kind of use case are you solving.
Then this use cases can be a drag application can be a text summarization.
Application can be a chat bot.
So based on different different use cases that actually depends on your requirements your company requirements.
So this is the first step.
You really need to define the use case that you're specifically doing.
Now with respect to this particular use case we usually take this entire module into the scope part
okay.
So this is basically my scope, right?
If I basically use a generic term.
Now once you define a use case, you let's say that I am going to probably develop a Rag application
in that I'm going to definitely use vector databases.
I may have a lot of PDF files.
I also need to probably convert that into a vectors and store it in some kind of vector store DB.
So some kind of use case you really need to define and all the requirements that is required in that
particular use case.
Coming to the next step, coming to the next step, which is super important because this steps will
be involving two important things okay.
And that is nothing but choosing.
Choosing the right model okay.
The right model.
When I say choosing the right model, here they are two different things that you can probably split
this into.
One, whether you are using or you are probably using some kind of foundation models.
So here I'm going to probably right.
Whether you are using foundation model and solving a use case.
So this is the one category that I would like to divide this particular module into.
The other category is that whether you want to build your own custom LLM right custom LLM.
Now see there are two things over here.
Right?
When I say foundation model.
Foundation models are already those larger models like OpenAI llama two.
Llama three.
Right.
You have Google Gemini Pro.
So these all are very huge foundation models.
And first, for most of the generic use cases, you can directly use those kind of foundation models
and you can solve the use case itself.
Right now with respect to this foundation models, we can also further go ahead and do fine tuning.
Right.
So let's say I have a fine I have a foundation model which I am specifically using to solve my business
use cases.
On top of that, if I really want to make this foundation model behave well for my own custom data,
then what I can do on top of this foundation model, I can use Laura Laura techniques and I can probably
fine tune all this kind of models.
Okay, so this is one of the step, the second step, the second step that I have written over here
as custom LM.
Custom LM is nothing, but it is building your LM from scratch.
Building your lm from scratch.
From scratch.
Right.
And obviously, uh, this one, there's a lot of benefit.
Also, if a company is building an LM model completely from scratch for this specific use cases, but
a lot of resources will definitely be required.
We have to really take care of model hallucination, many things and all as we go ahead.
But yes, uh, I've also seen many, many companies developing their own custom LM model.
Okay.
So choosing the right model or what kind of models you're specifically using to solve this particular
use cases, that becomes the second important module with respect to this gen AI project life cycle.
And obviously I've spoken about foundation models both in AWS.
You have in Google, you have in Microsoft Azure, you have currently Microsoft Azure.
How AI studio specifically have all the access of open AI services.
Obviously it is investing a huge amount of money over there.
Okay, now once you probably select the right kind of model, there are main three tasks that you probably
do for going forward.
Okay, so main three tasks okay.
The first task is nothing, but you can specifically use prompt engineering and solve a use case Prompt
engineering and solve a use case.
The second task that you actually can do is nothing but fine tuning, right?
Fine tuning, fine tuning.
So with the help of fine tuning, also, you can probably develop your own custom LM model.
And on top of that you can basically do it.
Let's say you're completely creating your LM model from scratch.
One more important mechanism that you have is nothing but, uh, aligning.
Uh, or you can probably say training with human feedback.
Training with human feedback.
And this is one of the very important step that is actually used while you are training your LM models.
How are LM model is basically trained.
I've already created a video in my playlist.
Uh, with respect to the link chain and all generative AI playlist, you can probably go head over there
fine tuning, how to specifically do fine tuning and all that.
Also, I've actually shown you the reason why I'm showing you this generative AI project life cycle.
Because tomorrow when I'm probably creating videos, um, now let's say in the upcoming videos, when
I'm creating videos related to this series over there, you'll be seeing all this particular steps going
ahead.
Okay.
Now, once you probably do all the steps, uh, the further step is something called as evaluation.
Okay.
Evaluation is basically seeing that how your model is performing by performing all this particular steps.
There are also different different performance metrics which we are probably going to follow.
Okay.
This two steps I would like to combine and say something like this okay.
So I'll say probably adapt and align models okay.
So this will be the specific model that, uh, we specifically use for this purpose.
Now over here, your model will be ready.
Everything is perfect.
Or you are able to solve the use cases.
Let's say your performance metrics is increasing over here.
So your metrics is specifically increasing.
And it is saying that now your model is ready.
Now it comes to the deployment part.
Right.
So with respect to the deployment part I would definitely say deployment.
Uh, further, you also need to do a lot of integration with different different applications.
So I will probably say application integration okay.
Application integration.
And here uh, what we do we specifically perform two major step one we optimize models Okay, uh, I'll
just write, optimize and deploy models.
Optimize and deploy models.
Okay.
And this deployment is specifically done for inferencing.
Okay.
And here is where most of your cloud platforms.
Here is where your LM ops.
LM ops is used.
You know, different, different inferencing techniques are there.
One technique I've already covered with respect to a platform, which is called as grok.
Right.
It uses a inferencing technique which is called as LP.
So it is always good idea that you should definitely know multiple ways of inferencing.
See, at the end of the day, whatever models you specifically create, unless and until the inferencing
is not fast, you definitely cannot use those things, right?
So it is very much necessary that you know the idea of this module extensively, because tomorrow building
all these things is very easy.
Fine tuning is very easy, right?
You definitely have a template, a framework, a data set, preparation and all.
And you can perform this particular step.
So that is the reason in this series of videos you'll be seeing that how much I will be focusing on
this lot of um, uh, LMS platform.
Right.
And I will also show you multiple platforms, like, which can definitely make your inferencing very
much good.
So this is the most important thing here.
Uh, definitely.
We'll be using AWS Azure.
Right.
You can use all these things GCP.
And we'll see what all services they have specifically provided probably for the inferencing purpose
again.
But initially our focus will definitely be on AWS.
Okay.
Then the second step.
Ah, um, after we do the deployment, uh, in the application integration, integration, what we do
next is that we build, uh, LLM powered application, LLM powered application powered application.
Because your integration is done, your API is created.
Now.
It's all how well you can actually build the solutions.
You can solve different different use cases and all.
So this overall gives a brief idea about the entire AI project life cycle.
Um, since, uh, we have already started this journey on cloud.
So this is necessary to know and uh, you should probably follow all the steps.
And whenever I create any videos with respect to any AI on AWS, all these steps will be considered
in mind and it will be shown to you.