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LLM-as-a-Judge is an increasingly popular way to evaluate the output of AI models. But is it actually worth using? And if so, how?
We're excited to announce a new integration between Label Studio Enterprise and IBM WatsonX, allowing you to access to the WatsonX.ai models and upload your labeled data into your WatsonX.data account.
Learn how to effectively evaluate multi-turn chats from your Gen AI-based chat system to unlock better performance and uncover potential issues.
Learn how Label Studio Plugins let you import external libraries, integrate services, automate actions, and build complex labeling logic in Label Studio with just a bit of JavaScript.
Learn how to use the ML Backend in Label Studio to speed up your labeling and improve time-to-insight using models like YOLO.
Use Prompts in Label Studio to generate synthetic data for training AI models, including RAG systems, faster and more efficiently.
Discover how to compare LLMs in Label Studio to select the best model for your data labeling tasks.
Like any AI, RAG can fail in a number of ways, which can impact your business. How do you know where your RAG is failing, and more importantly, how can you stop it before it creates problems?
Learn how to use Prompts in Label Studio to refine and automate pre-labeling with LLMs for more efficient data annotation.
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Join us for a live workshop to learn how you can easily autolabel NLP projects with Label Studio.
Implement a recurring HITL model evaluation workflow with Label Studio to keep a pulse on what’s really happening with your GenAI/ML models in production.
Learn how to develop high-quality domain-specific Q&A datasets for model fine-tuning.
Learn how to evaluate and fine-tune an Enterprise RAG Knowledge Base using Label Studio, ChatGPT, LangChain, and Ragas metrics.
Learn how to reduce your labeling costs by 5x while quadrupling labeling throughput with a high degree of accuracy using GenAI.
Learn why strong model evaluation workflows are essential for building reliable and trustworthy GenAI applications.
Whether you’re fine-tuning an LLM or building your own predictive model from scratch, your model is only as powerful and accurate as the quality of the data that you feed into it. That’s why we built Label Studio - to make getting quality data as efficient as possible.
Aaron Schliem, Sr. Solutions Architect at Welocalize, will talk us through the data design lifecycle - from defining the problem you’re trying to solve with data to building your data pipeline - and how to build out your annotation team within that context.
In this guide, you'll learn how to set up an audio labeling project in Label Studio.
Learn how Yext, a company that provides a suite of solutions that help organizations manage their online presence and deliver exceptional digital experiences, was able to more than double the number of queries their team was labeling every day while virtually eliminating data waste resulting from poor annotation quality.
In this mini-webinar, Heartex Sales Engineer Bernard Lawes will briefly walk you through the many ways that Label Studio Enterprise keeps your data safe and secure.
Quality data labeling, model training and model fine-tuning is critical to achieving AI/ML success. In this webinar, Bernard Lawes will talk through some of the issues that prevent data labeling programs from achieving their goals, and ways to overcome these obstacles.