TabPFN-3.5
TabPFN-3.5 is a tabular foundation model built for state-of-the-art predictions on structured data. It supports a wide range of prediction tasks, including churn, fraud, pricing, demand forecasting, risk, and other real-world data science problems, allowing teams to serve multiple use cases with one model. The model works with data as it is, handling missing values, outliers, categorical features, multi-table datasets, free text as a feature, thousands of distinct IDs without encoding, and hundreds of measurements per row. Users can feed in raw data, skip feature engineering and preprocessing, and get production-grade predictions from the first predict call. TabPFN-3.5 performs predictions in a single forward pass and is designed for both accuracy and speed, with fast inference for latency-critical predictive workflows. It supports production-scale datasets of up to one million rows natively and delivers 20x faster inference than previous model versions.
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Reka
Our enterprise-grade multimodal assistant carefully designed with privacy, security, and efficiency in mind. We train Yasa to read text, images, videos, and tabular data, with more modalities to come. Use it to generate ideas for creative tasks, get answers to basic questions, or derive insights from your internal data. Generate, train, compress, or deploy on-premise with a few simple commands. Use our proprietary algorithms to personalize our model to your data and use cases. We design proprietary algorithms involving retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to tune our model on your datasets.
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Amazon SageMaker Autopilot
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps.
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Evo 2
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework.
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