How can I use Snowpark to perform machine learning on my data in Snowflake?

To use Snowpark to perform machine learning on your data in Snowflake, you can follow these steps:

Create a Snowpark DataFrame from your data. You can do this by loading your data into a Snowflake table and then creating a Snowpark DataFrame from the table.
Apply machine learning algorithms to the Snowpark DataFrame. Snowpark supports a variety of popular machine learning algorithms, such as linear regression, logistic regression, and decision trees.
Train a machine learning model. You can use the Snowpark machine learning library to train a machine learning model on your Snowpark DataFrame.
Deploy the machine learning model. Once you have trained a machine learning model, you can deploy it to Snowflake.
Use the machine learning model to score new data. You can use the deployed machine learning model to score new data and make predictions.
Here is an example of how to train and deploy a machine learning model using Snowpark:

import snowpark
import numpy as np
import pandas as pd

# Load the pre-built machine learning model
model = snowpark.ml.load_model("my_model")

# Score new data
new_data = pd.DataFrame({"feature1": [1, 2], "feature2": [3, 4]})
predictions = model.predict(new_data)

# Print the predictions
print(predictions)

This code will load the pre-built machine learning model my_model from Snowflake and use it to score the new data. The predictions are then printed to the console.