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Learn how to develop high-quality domain-specific Q&A datasets for model fine-tuning.
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 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.
Learn how to use the new Data Discovery interface in Label Studio Enterprise to surface your most high-impact data in minutes, versus hours of manual work.
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.
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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.