Model

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.

Decrease your labeling time and costs. Rather than labeling from scratch, use model predictions - from your model or from a third party model - to visualize, select, and send as pre-labels to your labeling project in Annotate.

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).