Encord Active is an AI-powered platform that accelerates every stage of taking a computer vision model to production. It helps teams build better models faster by automating tasks such as data labeling, model validation, and dataset curation.
Features of Encord :
- Auto-find label errors: Encord Active automatically identifies and corrects label errors in training data, saving teams time and effort.
- Instantly search data with natural language search: Encord Active’s natural language search allows teams to quickly and easily find the data they need, regardless of its format or location.
- Debug models and boost performance: Encord Active helps teams identify and fix model errors, biases, and edge cases. It also provides insights into how data and labels impact model performance.
- Surface, curate, and prioritize the most valuable data: Encord Active helps teams identify and prioritize the data that is most valuable for training their models. This helps them to build better models with less data.
Benefits:
- Build better models faster: Encord Active automates many of the time-consuming tasks involved in model development, freeing teams to focus on more strategic initiatives.
- Improve model quality: Its AI algorithms help teams to identify and fix errors in their data and models. This results in higher quality models that are more accurate and reliable.
- Reduce costs: It can help teams to reduce the cost of model development by reducing the time and resources required.
- Accelerate time to market: By helping teams to build better models faster, it can help them to accelerate their time to market.
Use cases:
- Data labeling: Encord Active can be used to label data for a variety of computer vision tasks, such as object detection, image classification, and video segmentation.
- Model validation: it Active can be used to validate computer vision models and identify any areas where the model is struggling.
- Dataset curation: it can be used to curate datasets for computer vision models by removing outliers and ensuring that the data is representative of the real world.
- Active learning: it can be used to implement active learning strategies, which can help teams to build better models with less data.
- Model explainability: it can be used to understand how computer vision models make decisions. This can help teams to build more trustworthy and reliable models.
Overall, Encord Active is a powerful tool that can help teams build better computer vision models faster and more efficiently. It is a valuable asset for any team that is developing or deploying computer vision models.
