ML model training in Snowflake - Step-by-Step Execution

Process Flow - ML model training in Snowflake

Prepare Data

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Create Model

↓

Train Model

↓

Evaluate Model

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Use Model for Prediction

This flow shows the steps to train a machine learning model in Snowflake: prepare data, create the model, train it, evaluate, and then use it for predictions.

Execution Sample

Snowflake

CREATE OR REPLACE MODEL my_model
  OPTIONS (model_type='linear_regression') AS
SELECT feature1, feature2, target FROM training_data;

This code creates and trains a linear regression model named 'my_model' using features and target from 'training_data' table.

Process Table

Step	Action	SQL Command	Result
1	Prepare Data	SELECT feature1, feature2, target FROM training_data;	Data selected for training
2	Create and Train Model	CREATE OR REPLACE MODEL my_model OPTIONS (model_type='linear_regression') AS SELECT feature1, feature2, target FROM training_data;	Model 'my_model' created and trained
3	Evaluate Model	SELECT * FROM TABLE(EVALUATE_MODEL('my_model', 'training_data'));	Evaluation metrics returned
4	Use Model for Prediction	SELECT feature1, feature2, PREDICT('my_model', feature1, feature2) AS prediction FROM new_data;	Predictions generated for new data

💡 All steps completed successfully; model trained and predictions ready

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4
training_data	table with raw data	data selected with features and target	used to train model	used to evaluate model	unchanged
my_model	not created	not created	created and trained	evaluated	used for prediction
predictions	none	none	none	none	generated for new_data

Key Moments - 3 Insights

Why do we use SELECT inside CREATE MODEL?

The SELECT statement defines the data used to train the model. It tells Snowflake which features and target to use, as shown in execution_table step 2.

What happens if the model already exists?

Using CREATE OR REPLACE MODEL replaces the existing model with the new one, ensuring the latest training data and parameters are applied, as in step 2.

How do we check if the model is good?

We run EVALUATE_MODEL function (step 3) which returns metrics like accuracy or error to understand model performance.

Visual Quiz - 3 Questions

Look at the execution_table, what is the result after step 2?

A Model 'my_model' created and trained

B Data selected for training

C Predictions generated for new data

D Evaluation metrics returned

💡 Hint

Check the 'Result' column in execution_table row with Step 2

At which step do we generate predictions for new data?

A Step 1

B Step 3

C Step 4

D Step 2

💡 Hint

Look at the 'Action' and 'Result' columns in execution_table for prediction generation

If we change the model type to 'decision_tree', which step changes?

A Step 1 - Prepare Data

B Step 2 - Create and Train Model

C Step 3 - Evaluate Model

D Step 4 - Use Model for Prediction

💡 Hint

Model type is specified in the CREATE MODEL command in Step 2

Concept Snapshot

ML Model Training in Snowflake:
- Use CREATE MODEL with SELECT to define training data
- Specify model_type in OPTIONS
- Evaluate model with EVALUATE_MODEL function
- Use PREDICT function for new data
- CREATE OR REPLACE MODEL updates existing model

Full Transcript

This visual execution shows how to train a machine learning model in Snowflake. First, data is prepared by selecting features and target columns. Then, the CREATE OR REPLACE MODEL command creates and trains the model using that data. After training, the model is evaluated using the EVALUATE_MODEL function to get performance metrics. Finally, the trained model is used to generate predictions on new data with the PREDICT function. Variables like training_data and my_model change state through these steps, reflecting data selection, model creation, evaluation, and prediction. Key points include understanding the SELECT inside CREATE MODEL, replacing existing models, and checking model quality with evaluation. The quiz tests understanding of these steps and their results.

Practice

1. What is the main purpose of the CREATE MODEL statement in Snowflake's ML feature?

easy

A. To delete an existing machine learning model

B. To export model results to an external file

C. To make predictions using a trained model

D. To define and train a machine learning model using SQL

Solution

1. Step 1: Understand the role of CREATE MODEL

   This statement is used to define the model type, label column, and features, which means it sets up and trains the model.

2. Step 2: Differentiate from other commands

   PREDICT is used for predictions, not CREATE MODEL. Deleting or exporting models uses other commands.

3. Final Answer:

   To define and train a machine learning model using SQL -> Option D

4. Quick Check:

   CREATE MODEL = train model [OK]

Hint: Remember: CREATE MODEL means build and train [OK]

Common Mistakes:

• Confusing CREATE MODEL with PREDICT
• Thinking CREATE MODEL deletes models
• Assuming CREATE MODEL exports data

2. Which of the following is the correct syntax to create a logistic regression model named my_model in Snowflake?

easy

A. CREATE MODEL my_model AS logistic_regression WITH LABEL target AND FEATURES age, income;

B. CREATE MODEL my_model TYPE = 'logistic_regression' LABEL = target FEATURES = (age, income);

C. CREATE MODEL my_model USING logistic_regression LABEL target FEATURES (age, income);

D. CREATE MODEL my_model TYPE logistic_regression LABEL target FEATURES (age, income);

Solution

1. Step 1: Recall Snowflake's CREATE MODEL syntax

   The correct syntax uses TYPE = 'model_type', LABEL = label_column, and FEATURES = (feature_list) with equal signs and parentheses.

2. Step 2: Compare options

   CREATE MODEL my_model TYPE = 'logistic_regression' LABEL = target FEATURES = (age, income); matches the correct syntax exactly. Others have wrong keywords or missing equal signs or parentheses.

3. Final Answer:

   CREATE MODEL my_model TYPE = 'logistic_regression' LABEL = target FEATURES = (age, income); -> Option B

4. Quick Check:

   Correct syntax uses TYPE =, LABEL =, FEATURES = ( ) [OK]

Hint: Look for equal signs and parentheses in syntax [OK]

Common Mistakes:

• Omitting equal signs (=) in parameters
• Missing parentheses around features
• Using wrong keywords like AS or WITH

3. Given the following Snowflake SQL code:

CREATE MODEL churn_model TYPE = 'random_forest' LABEL = churn FEATURES = (age, balance, transactions);
SELECT * FROM PREDICT(churn_model, (SELECT age, balance, transactions FROM customers_test));

What will be the output of the PREDICT statement?

medium

A. The original customers_test table without predictions

B. An error because PREDICT cannot use SELECT statements

C. A table with predicted churn values for each row in customers_test

D. A summary report of model accuracy

Solution

1. Step 1: Understand the PREDICT statement

   PREDICT applies the trained model to new data and returns predicted values for each input row.

2. Step 2: Analyze the code

   The SELECT statement provides features from customers_test. PREDICT will output predictions for each row based on these features.

3. Final Answer:

   A table with predicted churn values for each row in customers_test -> Option C

4. Quick Check:

   PREDICT outputs predictions per input row [OK]

Hint: PREDICT returns predictions for each input row [OK]

Common Mistakes:

• Thinking PREDICT returns original data only
• Assuming PREDICT gives accuracy metrics
• Believing PREDICT cannot use SELECT

4. You run this Snowflake command:

CREATE MODEL sales_model TYPE = 'linear_regression' LABEL = sales_amount FEATURES = (store_size, location);

But get an error saying "Invalid feature column: location". What is the most likely cause?

medium

A. The column 'location' is not numeric or not suitable as a feature

B. The LABEL column 'sales_amount' does not exist

C. The model type 'linear_regression' is not supported

D. The FEATURES list must be empty for linear regression

Solution

1. Step 1: Understand feature requirements

   Features must be numeric or convertible to numeric for linear regression models.

2. Step 2: Analyze the error message

   "Invalid feature column" suggests 'location' is likely categorical or text, which linear regression cannot use directly.

3. Final Answer:

   The column 'location' is not numeric or not suitable as a feature -> Option A

4. Quick Check:

   Features must be numeric for linear regression [OK]

Hint: Check feature columns are numeric for regression models [OK]

Common Mistakes:

• Assuming LABEL column causes this error
• Thinking model type is unsupported
• Believing features list can be empty

5. You want to train a classification model in Snowflake to predict customer churn. You have a dataset with columns: customer_id, age, monthly_spend, churned (1 if churned, 0 otherwise), and signup_date. Which CREATE MODEL statement correctly trains a model ignoring customer_id and signup_date?

hard

A. CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = churned FEATURES = (age, monthly_spend);

B. CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = customer_id FEATURES = (age, monthly_spend, churned);

C. CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = churned FEATURES = (customer_id, age, monthly_spend, signup_date);

D. CREATE MODEL churn_model TYPE = 'linear_regression' LABEL = churned FEATURES = (age, monthly_spend);

Solution

1. Step 1: Identify correct label and features

   The label is churned because it indicates churn. Features should exclude customer_id and signup_date as they are identifiers or dates not useful for prediction.

2. Step 2: Choose appropriate model type

   Since churn is a binary outcome, logistic regression is suitable, not linear regression.

3. Final Answer:

   CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = churned FEATURES = (age, monthly_spend); -> Option A

4. Quick Check:

   Label = churned, features exclude IDs/dates, logistic regression for classification [OK]

Hint: Use label as target, exclude IDs/dates from features [OK]

Common Mistakes:

• Using ID or date columns as features
• Choosing wrong label column
• Using linear regression for classification