In this lesson, we are going to talk about life cycle management in the context, in the context of
LM ops.
So when you start looking for LM ops solutions and remember that we have already seen, uh, one of
them, which is, uh, Lang Lang Smith, if you remember, one of the, of the, uh, secondary solutions
that the team from Lang Chain provides.
Lang Smith.
But you have many others.
And right now this, this, this new industries is booming.
So you will find new and new and a different approaches here.
And one of the things you are going to listen from this kind of, uh, companies and marketing, uh,
approaches is going to be life cycle management.
So I think it's worthy to spend a few minutes talking about this.
So remember that the, the life cycle of a, uh, an application development is something that we have
been talking about, uh, before in this program.
So we will we if you remember, we we talked we talk about different stages like deployment, monitoring,
evaluation, fine tuning, etc., etc..
Right.
But uh, when you are talking about LM ops, we are talking about how to maintain an application in
each of the phases of the life cycle.
So we will consider each of the phases, and we will consider some criteria in order to optimize the
efficiency, the scalability, the risk mitigation, etc., etc..
So.
Let's talk for example, about a the deployment phase.
So the deployment phase involves implementing the LM application in a production environment.
This includes integrating the application with user interfaces, setting up the necessary infrastructure,
and ensuring that the model is ready to interact with end users.
At this stage, it will be crucial to consider aspects such as the anticipated workload and compatibility
with existing systems.
In the monitoring phase.
Once deployed, the application requires constant monitoring.
This involves tracking the model's performance, accuracy, response times, and resource consumption.
Monitoring also includes overseeing user interaction with the model to identify potential issues or
areas for improvement.
What about the evaluation phase?
Regular evaluation is vital to ensure that the model remains relevant and effective.
This may involve performance testing, analysis of user feedback and comparison of the model's results
with industry standards or business objectives.
And finally, the last phase we are going to a consider in this a quick summary.
Tuning the application.
So based on the results of monitoring and evaluation, the model may require adjustments.
This may include recalibrating the model, updating its training data, or modifying its parameters
to improve accuracy, reduce biases, or enhance user experience.
So from now on, when you listen to a LM.
Ops providers, a solution providers and you listen to words or read, uh, words like like life cycle
management, we are talking about that.
We are talking about how to optimize each of the phases of the life cycle of the application.
Okay.
In the next lesson, we are going to talk about a very important, uh, topic which is responsible AI
or responsible artificial intelligence.
This is not only important in the context of a society, you know, law and politics, but it is also
important in the context of developing your own application and a.
Consider some relevant things for your company and your reputation.
So let's talk about that in the next lesson.