Increase efficiency of manual data labeling with automated workflows and team performance management.
Ensure accuracy of ground truth datasets with reviewer workflows and quality reporting.
Use one platform for all data types and formats with templates and SDKs to easily configure labeling tasks.
Build differentiated, powerful and efficient models by fine tuning large language models with proprietary data and insights.
Data curation helps expand the model's target domain beyond the initially labeled dataset. By including diverse data from various sources, AI engineers expose the model to a wider range of examples, enabling better generalization and robustness when handling new and unseen data.
Fine-tuning AI models is an iterative process that involves a feedback loop of labeled data and additional data curation. This iterative approach ensures continuous refinement and optimization of the model's performance, increasing accuracy and adaptability over time.
Fine-tuned models benefit from data labeling by becoming more efficient in handling complex tasks. The labeled data provides valuable insights and guidance to the model, enabling it to navigate intricate scenarios, make informed decisions, and streamline processes. This improved efficiency saves time and resources, boosting productivity and operational effectiveness.
In this article we explore the profound impact of human feedback on AI and examine how powerful tools like Label Studio are catalyzing a paradigm shift in AI training methodologies.Read the Blog
Try the platform used by more than 250,000 data scientists and experts. Make your labeling team more efficient with workflows, analytics, annotator management tools. Simplify your labeling efforts by using the same platform to label any data type. And integrate any model, including foundation models like GPT-4 to automate your labeling and maximize the impact of the human signal your labelers provide.Explore the Platform