Accelerating ML ops, train faster, explore deeper on Jupyter Notebook/ TensorFlow

By Raz Kaplan

7.12.2022 twitter linkedin facebook

See how data scientists can run their data preparations and machine learning ops even faster using SQream’s connector to Jupyter Notebook Python and TensorFlow.

Presented by Richard Runds.

Massive data is the key to successful AI/ML. The more data used in the training, testing, and validation phases, the more accurate and significant the results. Yet most enterprise data scientists are able to utilize only a small portion of their exponentially growing data.

This example was done using SQream Python connector, learn more here

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