Snorkel AI

Use programmatic labeling to create a training dataset of fraudulent transactions.

Integrate expert-in-the-loop to review and correct labeling errors.

Employ meta-evaluation to ensure the reliability of fraud detection metrics.

Run realistic simulations to stress-test the model with edge-case scenarios.

Refine the model based on evaluation results.

Collect and curate a high-quality dataset of driving scenes using expert data services.

Use programmatic labeling to identify and annotate objects of interest.

Simulate adverse weather conditions to evaluate model robustness.

Employ expert correction and feedback to refine object recognition accuracy.

Measure behavior with realistic simulations and coding tasks.

Use programmatic labeling to create a dataset of customer inquiries and intents.

Integrate expert-in-the-loop to refine intent recognition accuracy.

Employ evaluator development to create custom metrics for chatbot performance.

Evaluate the chatbot using realistic customer service scenarios.

Refine the NLU model based on evaluation results.

Design agentic coding tasks.

Run rubric-guided evaluation pipelines.

Analyze failures and disagreement.

Update rubrics and target data collection.

Measure agentic behavior using terminal-grade coding tasks and simulations.

Curate high-quality, domain-specific datasets.

Apply programmatic labeling to flag potentially harmful content.

Use expert correction & feedback for edge cases.

Employ model-based & rule-based evaluation techniques.

Continuously refine the data development loop to adapt to evolving content patterns.

Learn to evaluate the output of your GenAI model.

Annotate datasets for better labeling.

Develop annotation workflow.

Create a prompt development workflow.

Annotate the datasets to inform programmatic labeling.