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Prior Labs offers pre-trained tabular foundation models for making predictions on structured data, enabling instant, accurate predictions without extensive model selection or tuning. It provides state-of-the-art results across various industries and use cases.
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Prior Labs provides pre-trained tabular foundation models, notably TabPFN, designed for making predictions on structured data with high accuracy and speed. The models are industry-agnostic, working across diverse use cases from finance to healthcare, and offer significant data efficiency, often performing as well as or better than other methods with 50% less data. Key features include instant, accurate predictions, no retraining needed (just update context with new data), and robust handling of missing values, outliers, and various data types. Prior Labs offers different versions like TabPFN-2.5 for datasets up to 50K samples, TabPFN Enterprise for advanced features like fine-tuning and real-time inference, and TabPFN-TS for time-series forecasting. The models can be deployed in various environments, including Microsoft Azure, AWS Sagemaker, and Databricks, or accessed via API.
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Open Source · Enterprise
Not publicly disclosed. Check priorlabs.ai for current pricing.
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