Foundry enables teams to use world-class foundation models to enrich datasets and automate tasks. In a few clicks, use this AI copilot to explore, test, and integrate powerful models into your workflow.
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's data engine automates data curation and labeling from 1B+ assets
Blue River Technology needed to rapidly scale and optimize their computer vision model development pipeline and decrease their iteration cycles — which often took several weeks — to hours in order to deliver the best AI-powered products. Two of the primary causes of delay in their processes were data management and infrastructure being created and maintained by ML engineers and an arduous data curation process that took longer and became more painful as the amount of data increased exponentially.
The team built a unified machine learning and data engine that leverages embedded integrations with best-in-class data storage and management, data curation, and labeling solutions. The platform also includes multiple robust and innovative applications designed to increase efficiencies and reduce ML engineering workloads.
With the new data engine, Blue River Technology’s ML teams can now spend more of their time focusing on training, monitoring, and maintaining their computer vision models. Their data scientists can pull updated, refined, relevant datasets for every use case and model within minutes via Labelbox Catalog.
How Ancestry prioritizes collaboration and training data quality to enable genealogical breakthroughs with ML
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