 Hi, Sanjita. You are up. Hello. This is Seshita. I'm going to talk about one of the open source tool, which is Label Studio. If you are, you know, like working on data sense projects, you know, like doing the supervised machine learning, one of the requirement is the label data. And particularly if you're working with business users trying to get the label data is going to be very difficult. And, you know, like even creating a UI for each of the, you know, like use case is going to be difficult. So Label Studio is an open source platform, which you can use it for labeling tasks in many ways. I'm, you know, this is the label studio data. They have a just quick demo is there. I'll just show you how easy to really set this up, you know. So there are only four tasks are there. You just set up your config, import your tasks, do your job, or give it to people to really do their job and then export, you know, the output out of it. So if I go to setup, there are a lot of different type of, you know, like labeling tasks are here, you know, like audio classification, image classification, text classification, multi-label classification. There are many of them are there. I hope it is going to be, you know, like covering the lot of universe of this labeling task. Then if you really get onto that, I'm just using one of the, you know, simple example of text classification. Let's assume that you have data which you want to classify as a positive, negative or neutral sentiment. You have a lot of this data and you want to give it to users to get this, you know, like labeled. So this is the way it's look like if you look at the interface preview here. Once you have set up, just go and save this. So now it will ask you to import your task. So you can just have a handy file that you can really import it, you know, like into this tool. And it'll tell you there are three tasks are there right now. And then you can start labeling it. So if you look at this is the way it's going to appear to the user. It can be, you know, like given to a lot of users, there are a lot of ways how this data can be, you know, like who, what data can go to different users. You can do a lot of other stuff also in this. But the user when it's really logging, he will look at all these things and then say now, I mean, this, this is a negative sentiment. I just label the data and I submit my, you know, this one. Similarly, this is a positive sentiment. And then I submit it. And then I have a neutral one. I submit it. Now I'm done with my, you know, labeling, and you can go and, you know, like export this data. So data that supports multiple formats that, you know, like you can really go and export based on, you know, you need. As well as it also has another capability where you can go back and then give a model to it. As you already have a base model that you have really built it. It starts applying the model and give the recommendation to the users and then user can really either go with it or they can override that, you know, like labeling tasks also. And this is an open source. And then it can be, you know, like either, you know, like hosted on your own on-premises and then you can really use it. I think this is a quick introduction of the label studio. Hope this is going to be useful if you're doing the, you know, supervised machine learning and you want to give your data for the users to label it. Thank you.