• 🎥 Intro Video Walkthrough: Snowflake for ML Intro
• 🎥 End-to-End ML Ops in Snowflake: Live: End-to-End ML Ops in Snowflake
• 🔗 Regular 30-Day Trial: Sign Up
• 🔗 Student/Educator 120-Day Trial: Sign Up (Student)

Although we recorded videos, we are constantly making upgrades and additions to this repo, so the videos may differ slightly from what is in the repo. Overall they are the same but we will continue to upload more videos on any additions to the repo.

1. Create a .env file and populate it with your account details:

   SNOWFLAKE_ACCOUNT = abc123.us-east-1
   SNOWFLAKE_USER = username
   SNOWFLAKE_PASSWORD = yourpassword
   SNOWFLAKE_ROLE = sysadmin
   SNOWFLAKE_WAREHOUSE = compute_wh
   SNOWFLAKE_DATABASE = snowpark
   SNOWFLAKE_SCHEMA = titanic
   

2. Utilize the environment.yml file to set up your Python environment for the demo:

   • Examples in the terminal:
     • conda env create -f environment.yml
     • micromamba create -f environment.yml -y

Why we partner with Anaconda

Review of distributed Hyperparameter tuning benefits

Local run time 8 min 27 seconds

SnowflakeML run time 1 min 17 seconds (6.5x improvement in speed leveraging a Large WH)

Execute the load_data notebook to accomplish the following:

• Load the Titanic dataset from Seaborn, convert to uppercase, and save as CSV
• Upload the CSV file to a Snowflake Internal Stage
• Create a Snowpark DataFrame from the staged CSV
• Write the Snowpark DataFrame to Snowflake as a table

In the snowml notebook:

• Generate a Snowpark DataFrame from the Titanic table
• Validate and handle null values
• Remove columns with high null counts and correlations
• Adjust Fare datatype and impute categorical nulls
• One-Hot Encode Categorical Values
• Segregate data into Test & Train sets
• Train an XGBOOST Classifier Model with hyperparameter tuning
• Conduct predictions on the test set
• Display Accuracy, Precision, and Recall metrics

Following the load_data steps, utilize the deployment notebook to:

• Create a Snowpark DataFrame from the Titanic table
• Assess and eliminate columns with high null counts and correlated columns
• Adjust Fare datatype and handle categorical nulls
• One-Hot Encode Categorical Values
• Split the data into Test & Train sets
• Train an XGBOOST Classifier Model, optimizing with grid search
• Display model accuracy and best parameters
• Register the model in the model registry
• Deploy the model as a vectorized UDF (User Defined Function)
• Execute batch predictions on a table
• Perform real-time predictions using Streamlit for interactive inference