From the course: Intro to Snowflake for Devs, Data Scientists, Data Engineers
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Snowflake ML overview: Part 2 - Snowflake Tutorial
From the course: Intro to Snowflake for Devs, Data Scientists, Data Engineers
Snowflake ML overview: Part 2
- We've talked about how you can do ML in seconds with the Snowflake Cortex ML functions. Now let's talk about how to do ML in minutes with Snowflake. There are a bunch of things to talk about, so we'll just cover each briefly. First, I want to point out some terminology. Snowpark ML Modeling, the Snowflake Feature Store, and the Snowflake Model Registry all have APIs that are accessible from the unified Snowpark ML library. You can access APIs for each of these three features by installing the Snowpark ML library in Python, and you can do this from your preferred notebook or IDE, including Snowflake notebooks, which we'll talk about in a second. A big part of Snowpark ML are the Snowpark ML modeling APIs, which are based on standard Python frameworks like scikit-learn and XGBoost. You can see this code example on the side. You can use Snowpark ML modeling APIs for pre-processing data, feature engineering, and training models inside Snowflake, which is great because that means you…
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