Covering everything you need to know in order to build AI products faster.
How to uncover model errors with Quantumworks Lab Model
Explore how to perform model error analysis using Quantumworks Lab, with a specific focus on utilizing the Gallery View and Model Metrics.
Using Meta's Segment Anything (SAM) model on video with Quantumworks Lab's model-assisted labeling
Learn how to use Meta’s Segment Anything (SAM) model with YOLOv8 to automatically detect, classify, and draw masks on video.
How to fine-tune large language models (LLMs) with Quantumworks Lab
Learn how to iterate and rapidly fine-tune OpenAI large language models with Quantumworks Lab Model.
An introduction to model metrics
Learn how you can use model metrics to surface low-performing classes, find and fix labeling errors, and improve the overall performance of the model before it hits production on real-world data.
Using Quantumworks Lab and Weights & Biases to fine tune your computer vision projects
Learn how you can use Quantumworks Lab and Weights & Biases together to build better computer vision models. Follow a step-by-step workflow of data curation, annotation, model diagnostics and hyperparameter tuning.
How to get started in Quantumworks Lab Model: Train, evaluate, and improve your ML models
Learn how to ship better models faster by leveraging Quantumworks Lab Model. In this guide, we'll walk you through a COCO object detection example to get you onboarded in Model with your first project, model, and model run.
Get started with active learning
Discover how to get started with active learning by leveraging the 3 techniques that consistently help ML teams more quickly identify what data will most dramatically improve model performance.
How to find and fix label errors
Learn how you can use Quantumworks Lab Model to visually compare your ground truths and predictions to identify and fix label errors.
How to curate and version your training datasets and hyperparameters
Learn how you can use Model to configure, track, and compare essential model training hyperparameters alongside training data and data splits. Easily track and reproduce model experiments to observe the differences and share best practices with your team.
How to find and fix model errors
A great way to boost model performance is to surface edge cases on which the model might be struggling. You can fix those model failures with targeted improvements to your training data so that the model is better trained on these edge cases.
