Snowflake Deployments require connecting to your instance. This procedure is described in Setting up Integrations - Snowflake. Once that is configured you will have the option to deploy to snowflake, that will be described here.
For this example, we’ll use a restaurant review dataset that’s located on Snowflake, which has just two columns: “Reviews,” which are strings of restaurant reviews, and “Liked,” the binary column of “0” or “1” that we’re trying to predict. Since we’ve integrated Snowflake, we can simply search for and select this dataset.
Again, we can make predictions in the same way now as any other flow. Let’s hit “Add Step” and then the “Liked” column to predict whether a review has positive or negative sentiment.
In seconds, we’ll have a highly accurate model to predict restaurant review sentiment.
Finally, we can deploy our model in virtually any environment. Since we’ve already integrated with Snowflake, let’s deploy our model back to Snowflake. What this means is that we can automatically make predictions on new data in Snowflake. Simply hit “Add Step,” and then select Snowflake in the “Outputs” section.
The final step is to simply fill out the deployment settings, and ensure that the fields are mapped correctly to the new data. That’s it! When you’re done, you can hit “Show Preview” and “Deploy.”