Tutorials

Understanding how your production model is performing is the most crucial yet most challenging step in the Machine Learning pipeline. This tutorial gives you an easy way to keep a pulse on what is really happening in production.

OpenAI’s new Structured Outputs feature allows you to ensure outputs conform to a defined JSON structure. In this blog, we’ll explore how to leverage this feature for various labeling tasks.

In this article, we want to demonstrate a method of curating large datasets to reduce but not remove the cost for curating a high quality medical Q&A dataset in Label Studio and fine-tuning Llama 3 on this data.

This article is part of a longer series that will teach you how to develop and optimize a question answering (QA) system using Retrieval-Augmented Generation (RAG) architecture. In this tutorial, we are going to show you how to create a generator that builds responses based on those documents.

In this introduction to our tutorial series on optimizing RAG pipelines, we'll introduce an example question answering (QA) system leveraging a Retrieval-Augmented Generation (RAG) architecture and outline three methods for optimizing your RAG pipeline utilizing Label Studio.

Interactively Label Image-to-Text Datasets with Label Studio and Tesseract OCR.

Pair Grounding Dino or Grounding SAM models with Label Studio to automatically perform object identification and image segmentation tasks using text labels.

Learn how to build an image annotation workflow using Label Studio and Segment Anything in Hugging Face Spaces!

Dive into the top 5 open dataset resources that could be the key to your next project's success

With the latest 1.8 release — we launched the all-new Generative AI template library — a set of templates designed for you to work with foundation models and adapt them to your needs, right within Label Studio. Get started with fine-tuning models such as Llama 2, GPT-4, Whisper, Alpaca, and PaLM 2 among others.

Learn how to automate labeling tasks using GPT-4 with the Label Studio Machine Learning backend.

Learn the background knowledge and steps required to integrate Machine Learning models into your Label Studio workflow.

Brand new to Label Studio? We’ve created a jam-packed new tutorial with the most important information to get you up and running.

Sentiment analysis provides consistent, data-driven insights for businesses looking to better understand their users and products. Here’s how to get started.

Use webhooks from open source data labeling software Label Studio to seamlessly integrate your Amazon SageMaker model development workflow for your machine learning and data science projects using AWS Lambda, Amazon S3, and public domain bird images.

Use open source data labeling software Label Studio to quickly create YOLO v3 and v4 compatible datasets for training purposes and image labels for image object detection data science and machine learning projects.

Use open source data labeling software Label Studio to improve optical character recognition (OCR) results for receipts, invoices, menus, signs, and other important images processed with Tesseract and Python.

Use open source data labeling software Label Studio to improve audio transcriptions of customer support calls, video conference meetings, and other audio recordings.

Use Label Studio to evaluate named entity recognition parsers like spaCy and revise predictions by annotating a gold standard dataset for your data science and machine learning projects.