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By a suitable arrangement of relays any possible combination of the data recorded on the cards may be counted.

Herman Hollerith, 1889

Hollerith spent his career proving that tables deserve their own machine. We think he was right. Traditional data science/ML treats every table as a new problem. You clean and encode the data, engineer features, train a model, tune it and repeat until the results are good enough. Hollerith is already pretrained on billions of tables, so it understands how tabular data behaves. Using Hollerith, you skip the pipeline entirely: fit on your data, predict in one forward pass, done. The usual loop cleans, encodes, engineers features, trains, tunes, evaluates and re-trains before it predicts. With Hollerith, fit and predict are the whole pipeline, and the table and rows are purged when the job finishes. The usual loop cleans, encodes, engineers features, trains, tunes, evaluates and re-trains before it predicts. With Hollerith, fit and predict are the whole pipeline, and the table and rows are purged when the job finishes. Because there is no per-dataset training loop, you get predictions in seconds instead of hours, with no feature engineering or hyperparameter tuning. And because one pretrained model works across datasets, the same two lines of code work whether your table holds sales figures, sensor readings, or patient records.

At a glance

Next steps

Quickstart

Make your first API call to Hollerith

Classification

Learn about Hollerith’s classification capabilities

Evaluating accuracy

Learn how Hollerith scores itself

Limits

Learn about the constraints Hollerith is optimized for