Identifying and fixing model errors with improved training data is the key to building high-performing models. Conduct powerful error analysis to surface model errors, diagnose root causes, and fix them with targeted improvements to your training data. Collaboratively version, evaluate, and compare training data, hyperparameters, and models across iterations in a single place.
Data quality issues can severely undermine your model’s performance. Use your model as a guide to find and fix labeling mistakes, unbalanced data classes, and poorly crafted data splits that can affect model performance.
Not all data impacts model performance equally. Leverage your data distribution, model predictions, model confidence scores, and similarity search to curate high-impact unlabeled data that will boost your model performance.
Simplify your data-to-model pipeline without friction. Seamlessly integrate Labelbox with your existing machine learning tech stack using our Python SDK. Labelbox Model works with any model training and inference framework, major cloud providers (AWS, Azure, GCS), and any data lake (Databricks, Snowflake).
How Blue River Technology used model-assisted labeling to reduce labeling costs by 50%
High labeling costs associated with the costs of crops vs. weeds on full image segmentation images.
Labelbox’s model-assisted labeling and collaborative annotation suite.
The company is now able to standardize how they create and manage data all in a single location and using Labelbox's automation suite, they’ve seen a reduction in labeling times by 50%, worth millions of dollars in cost savings per year.
How Burberry harnesses Labelbox and Databricks to curate their strategic marketing assets
Retail and ecommerce
How Ancestry prioritizes collaboration and training data quality to enable genealogical breakthroughs with ML
Technology and software