From the course: Intro to Snowflake for Devs, Data Scientists, Data Engineers
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Snowpark ML modeling: Part 2 - Snowflake Tutorial
From the course: Intro to Snowflake for Devs, Data Scientists, Data Engineers
Snowpark ML modeling: Part 2
- If you've been following along, you'll know we've got our data all set up. Now we're ready to try to use Snowpark ML to predict which neighborhood that sneaky food truck will choose to go to on any given day in the future. Let's get right back into it. Snowpark ML has a few parts to it, but we're going to focus here on Snowpark ML modeling. If you look at the docs for Snowpark ML, you'll see that what it's actually doing is incorporating lots of the most important functions and methods for many of the most important open source Python ML libraries, likescikit-learn, XGBoost, and LightGBM, so that they're really easy to use in Snowflake. One example of a way this is really helpful is that there are lots of moments where if you call one of these models in Snowflake, training that model happens in a distributed way automatically. Meaning it can cut your data up and train on different parts of it at the same time. This parallelization can really speed up the training process. So here…
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