Label Studio is an open-source data labeling tool that offers flexibility for labeling all types of data. It supports computer vision, natural language processing, speech, voice, and video models, making it a versatile tool for preparing training data.Some key features of Label Studio include flexible data labeling for all data types, customizable tags and labeling templates, integration with ML/AI pipelines via webhooks, Python SDK, and API, ML-assisted labeling with backend integration, connectivity to cloud object storage (S3 and GCP), advanced data management with the Data Manager, support for multiple projects and users, and trusted by a large community of Data Scientists.The use cases of Label Studio are extensive and include preparing training data for computer vision models, natural language processing models, speech and voice models, and video models. It can also be used for the classification of images, audio, text, and time series data, object detection and tracking in images and videos, semantic segmentation of images, speaker diarization, emotion recognition in audio, audio transcription, document classification, named entity extraction, question answering, sentiment analysis, time series analysis, event recognition, dialogue processing, optical character recognition, and multi-domain applications requiring various types of data labeling.To use Label Studio, you can follow these steps: install the Label Studio package through pip, brew, or clone the repository from GitHub, launch Label Studio using the installed package or Docker, import your data, choose the data type and labeling task, start labeling with customizable tags and templates, connect to your ML/AI pipeline using webhooks, Python SDK, or API, explore and manage your dataset in the Data Manager, and support multiple projects, use cases, and users within the Label Studio platform.