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