This blog post was originally published at 3LC’s website. It is reprinted here with the permission of 3LC.
AI performance isn’t just about better architectures or more compute – it’s about better data. Even perfectly labeled datasets can hold hidden inefficiencies that limit accuracy. See how teams use 3LC to refine datasets, optimize labeling strategies, and make every training sample count.
The difference between an average AI model and a superior one doesn’t just come from better architecture or more compute – it comes from better data. Even if your training data is 100% correctly labeled, that doesn’t mean it’s optimal for model performance. That’s where 3LC comes in.
3LC is a no-nonsense turn-key solution for real-time debugging, diagnosis, and improvement of training data. It gives AI teams unprecedented insight into how their models react to their training data and provides a clear, iterative workflow to make every training sample count.
Currently optimized for Computer Vision, 3LC is built on a robust core that will extend its support to LLMs, RAGs, and Agentic performance down the road.
What is 3LC?
At its core, 3LC is a comprehensive platform that streamlines the process of training data improvement. It focuses on one simple yet key principle: better data leads to better models. By leveraging a real-time feedback loop, users of 3LC can continuously evaluate their training data, identify weaknesses, and apply precise, targeted improvements. This approach not only boosts model accuracy but also helps maintain lean and efficient data pipelines.
3LC is a turn-key solution for debugging, diagnosis and improvement of data-related issues for AI.
How Does 3LC Work?
3LC’s process can be visualized as an iterative cycle:
- Metrics Capture During Training: 3LC continuously collects detailed per-sample metrics across epochs, tracking how each data point influences model learning over time. This real-time insight lays the foundation for understanding data quality issues.
- Diagnosis & Debugging: Using these insights, users can analyze patterns, identify mislabeled or hard-to-learn samples, and spot underrepresented cases that impact model performance. 3LC helps surface the right information, but users decide where and how to intervene.
- Data Enhancement: Once issues are identified, users can refine or add labels, or introduce targeted data modifications – ensuring that every training sample contributes meaningfully to model performance.
Who is 3LC For?
3LC is designed to be a versatile tool, catering to a wide range of ML teams and scenarios. Here’s how it benefits different users:
Teams That Have Iterated on Training Data but Still Need Higher Accuracy
Even when a dataset is meticulously verified as error-free, models can fall short of their potential. 3LC bridges this gap by identifying issues that traditional quality checks miss, leading to significant improvements in model performance.
Left picture: Labels for bird nests. Right Picture: Filtered in models’ wrong predictions of bird nests. This feature is always present inside the Label to the left as well, so the model struggles to learn that this feature alone is different.
Solution: In one click, adding these 868 wrongly predicted labels as “bolts”, to guide the model better.
Teams Looking to Diagnose Model Performance Issues
Understanding why a model isn’t performing optimally can be challenging. 3LC offers complete visibility into model performance, pinpointing specific areas where data may be lacking and suggesting targeted enhancements for improved accuracy.
Filtered in the samples that took the longest for the model to learn during training, based on total distance traveled in embedding space across epochs. From here, you can either add more similar samples or increase their weight, guiding the model to focus more on these critical examples.
Teams Seeking the Optimal Labeling Strategy
When launching new projects or transitioning to different model architectures, the ideal data labeling strategy may vary. 3LC guides you in selecting the optimal labeling approach, ensuring that datasets are perfectly aligned with model requirements. For example, will it help your model to distinguish similar objects if labels encompass a bit of the area surrounding the labeled object?
Teams Needing Quick Label Updates
Quickly adjust inaccurate labels based on the model’s feedback, ensuring your training data is perfect.
Label to the left, prediction to the right. Filtered in those predictions with high confidence but a lower IOU range. They are more precise. Replace 100’s of labels like this with a single click.
Teams Training on Synthetic Data
More synthetic data isn’t always better. 3LC pinpoints which synthetic samples actually enhance model performance. And the challenges with how and what data should be labeled for synthetics are the same as with regular data.
Teams Targeting Smaller, More Efficient Models
Smaller models require highly relevant datasets to perform well. 3LC refines data to ensure that even a reduced dataset drives high accuracy, enabling the deployment of efficient models without compromising performance.
Teams Handling Continuous Data Streams and Optimizing Pipeline Efficiency
If you regularly receive fresh data, 3LC helps you identify and label the most impactful samples to optimize dataset size without compromising quality – ensuring a leaner, more efficient data pipeline.
Use the model’s embedding space to reduce dataset size or filter incoming data before applying 3LC’s active learning and advanced model-assisted labeling, ensuring the most diverse and high-confidence samples are labeled first.
The Transformational Benefits of 3LC
Integrating 3LC into your workflow delivers transformational benefits:
- Higher Model Accuracy: By enhancing data quality, 3LC helps models perform better, unlocking higher levels of accuracy that translate into tangible real-world impact.
- Smaller, Efficient Data Pipelines: The training process is optimized by reducing data volume while maintaining quality, leading to faster iterations, labeling and cost savings.
- Agility in Data Management: Real-time insights and rapid feedback allow teams to quickly adapt to new challenges and opportunities in model development.
- Empowered Decision Making: Deep insights into specific data issues enable precise, targeted improvements
Looking Ahead
While 3LC is currently optimized for Computer Vision, its robust and adaptable workflow is designed with future expansion in mind. Planned enhancements will extend support to LLMs, RAGs, and Agentic performance, ensuring that 3LC remains at the forefront of data quality enhancement as the platform evolves.
Conclusion
In the world of AI, better data truly leads to better models. 3LC stands out as a game-changing solution that transforms the approach to training data quality. By offering real-time insights, targeted debugging, and seamless data enhancement, 3LC empowers teams to achieve higher model accuracy and efficiency. Every example discussed above is based on real-world applications, where clients have successfully improved their models, using 3LC. While currently delivering breakthrough results in Computer Vision, the same data-driven improvements will extend to LLMs, RAGs, and Agentic workflows, ensuring optimal performance across a wide range of AI models.
Paul Andresen
CEO and CTO, 3LC
