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you'll learn how to build and deploy a complete machine learning workflow entirely within \u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E. You'll work through a mortgage lending prediction use case, implementing each stage of the ML lifecycle from feature engineering to model deployment and monitoring.\u003C/p\u003E\n","\u003Cp\u003EThis tutorial showcases Snowflake's ML capabilities, including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFeature Store for defining and managing features\u003C/li\u003E\u003Cli\u003ESnowflake ML APIs for model training and hyperparameter optimization\u003C/li\u003E\u003Cli\u003EModel Registry for versioning and lifecycle management\u003C/li\u003E\u003Cli\u003EML Observability for tracking performance and drift\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to build a model in Snowflake Notebooks on Container Runtime\u003C/li\u003E\u003Cli\u003EHow to deploy a model for inference seamlessly with Snowflake Model Registry\u003C/li\u003E\u003Cli\u003EHow to monitor a model during production with ML Observability\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EYou'll build a complete mortgage lending prediction system that:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEngineers features from loan application data\u003C/li\u003E\u003Cli\u003ETrains baseline and optimized XGBoost models\u003C/li\u003E\u003Cli\u003ERegisters models with metadata and metrics\u003C/li\u003E\u003Cli\u003EMonitors model performance over time\u003C/li\u003E\u003Cli\u003EProvides model explanations using SHAP values\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with ACCOUNTADMIN access.  Sign up for a 30-day free trial account, if required.\u003C/li\u003E\u003Cli\u003EBasic understanding of Python and machine learning concepts\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EFirstly, run this SQL setup script to create the necessary objects:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Using ACCOUNTADMIN, create a new role for this exercise \nUSE ROLE ACCOUNTADMIN;\nSET USERNAME = (SELECT CURRENT_USER());\nSET ALLOW_EXTERNAL_ACCESS_FOR_TRIAL_ACCOUNTS = TRUE;\nCREATE OR REPLACE ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- Grant necessary permissions to create databases, compute pools, and service endpoints to new role\nGRANT CREATE DATABASE on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE; \nGRANT CREATE COMPUTE POOL on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\nGRANT CREATE WAREHOUSE ON ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\nGRANT BIND SERVICE ENDPOINT on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- grant new role to user and switch to that role\nGRANT ROLE E2E_SNOW_MLOPS_ROLE to USER identifier($USERNAME);\nUSE ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- Create warehouse\nCREATE OR REPLACE WAREHOUSE E2E_SNOW_MLOPS_WH WITH WAREHOUSE_SIZE='MEDIUM';\n\n-- Create Database \nCREATE OR REPLACE DATABASE E2E_SNOW_MLOPS_DB;\n\n-- Create Schema\nCREATE OR REPLACE SCHEMA MLOPS_SCHEMA;\n\n-- Create compute pool\nCREATE COMPUTE POOL IF NOT EXISTS MLOPS_COMPUTE_POOL \n MIN_NODES = 1\n MAX_NODES = 3\n INSTANCE_FAMILY = CPU_X64_M;\n\n-- Using accountadmin, grant privilege to create network rules and integrations on newly created db\nUSE ROLE ACCOUNTADMIN;\n-- GRANT CREATE NETWORK RULE on SCHEMA MLOPS_SCHEMA to ROLE E2E_SNOW_MLOPS_ROLE;\nGRANT CREATE INTEGRATION on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\nUSE ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- Create an API integration with Github\nCREATE OR REPLACE API INTEGRATION GITHUB_INTEGRATION_E2E_SNOW_MLOPS\n   api_provider = git_https_api\n   api_allowed_prefixes = ('https://github.com/Snowflake-Labs')\n   API_USER_AUTHENTICATION = (TYPE = SNOWFLAKE_GITHUB_APP)\n   enabled = true\n   comment='Git integration with Snowflake Demo Github Repository.';\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate workspace\u003C/h3\u003E\n","\u003Cp\u003ENow we can navigate to the Workspaces tab in Snowsight to create a git based workspace!\u003C/p\u003E\n","\u003Cp\u003EFrom the workspace drop down, choose create from Git repository:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/workspace_setup1.png?v=89cbbeb6\" alt=\"git-workspace1\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECreate the workspace based on the repository url https://github.com/Snowflake-Labs/sfguide-build-end-to-end-ml-workflow-in-snowflake.git\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/workspace_setup2.png?v=89cbbeb6\" alt=\"git-workspace2\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, create a service to run the notebook on the compute pool created by the setup:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/create_service1.png?v=89cbbeb6\" alt=\"create-service1\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/create_service2.png?v=89cbbeb6\" alt=\"create-service2\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBe sure to run this with the newly created role \u003Cstrong\u003EE2E_SNOW_MLOPS_ROLE\u003C/strong\u003E and warehouse \u003Cstrong\u003EE2E_SNOW_MLOPS_WH\u003C/strong\u003E!\u003C/p\u003E\n","\u003Ch3\u003EInitialize Snowflake Session and Variables\u003C/h3\u003E\n","\u003Cp\u003ESet up your environment variables and initialize your Snowflake session:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Update this VERSION_NUM to version your features, models etc!\nVERSION_NUM = '0'\nDB = "E2E_SNOW_MLOPS_DB" \nSCHEMA = "MLOPS_SCHEMA" \nCOMPUTE_WAREHOUSE = "E2E_SNOW_MLOPS_WH" \n\nimport pandas as pd\nimport numpy as np\nimport sklearn\nimport math\nimport pickle\nimport shap\nfrom datetime import datetime\nimport streamlit as st\nfrom xgboost import XGBClassifier\n\n# Snowflake ML\nfrom snowflake.ml.registry import Registry\nfrom snowflake.ml.modeling.tune import get_tuner_context\nfrom snowflake.ml.modeling import tune\nfrom entities import search_algorithm\n\n#Snowflake feature store\nfrom snowflake.ml.feature_store import FeatureStore, FeatureView, Entity, CreationMode\n\n# Snowpark session\nfrom snowflake.snowpark import DataFrame\nfrom snowflake.snowpark.functions import col, to_timestamp, min, max, month, dayofweek, dayofyear, avg, date_add, sql_expr\nfrom snowflake.snowpark.types import IntegerType\nfrom snowflake.snowpark import Window\n\n#setup snowpark session\nfrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoad and Prepare Data\u003C/h3\u003E\n","\u003Cp\u003ELoad the mortgage lending demo data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etry:\n    print("Reading table data...")\n    df = session.table("MORTGAGE_LENDING_DEMO_DATA")\n    df.show(5)\nexcept:\n    print("Table not found! Uploading data to snowflake table")\n    df_pandas = pd.read_csv("MORTGAGE_LENDING_DEMO_DATA.csv.zip")\n    session.write_pandas(df_pandas, "MORTGAGE_LENDING_DEMO_DATA", auto_create_table=True)\n    df = session.table("MORTGAGE_LENDING_DEMO_DATA")\n    df.show(5)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMake sure your Snowflake account has the necessary privileges to create tables and execute ML operations\u003C/li\u003E\u003Cli\u003EEnsure your warehouse is properly sized for ML workloads\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFeature Engineering\u003C/h2\u003E\n","\u003Cp\u003EIn this section, we'll create features from our raw mortgage lending data using Snowpark APIs.\u003C/p\u003E\n","\u003Ch3\u003ECreate Feature Transformations\u003C/h3\u003E\n","\u003Cp\u003EFirst, let's examine the time range of our data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edf.select(min('TS'), max('TS'))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow, let's create a dictionary of feature transformations:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Create a dict with keys for feature names and values containing transform code\nfeature_eng_dict = dict()\n\n#Get current date and time\ncurrent_time = datetime.now()\ndf_max_time = datetime.strptime(str(df.select(max("TS")).collect()[0][0]), "%Y-%m-%d %H:%M:%S.%f")\n\n#Find delta between latest existing timestamp and today's date\ntimedelta = current_time- df_max_time\n\n#Timstamp features\nfeature_eng_dict["TIMESTAMP"] = date_add(to_timestamp("TS"), timedelta.days-1)\nfeature_eng_dict["MONTH"] = month("TIMESTAMP")\nfeature_eng_dict["DAY_OF_YEAR"] = dayofyear("TIMESTAMP") \nfeature_eng_dict["DOTW"] = dayofweek("TIMESTAMP")\n\n#Income and loan features\nfeature_eng_dict["LOAN_AMOUNT"] = col("LOAN_AMOUNT_000s")*1000\nfeature_eng_dict["INCOME"] = col("APPLICANT_INCOME_000s")*1000\nfeature_eng_dict["INCOME_LOAN_RATIO"] = col("INCOME")/col("LOAN_AMOUNT")\n\ncounty_window_spec = Window.partition_by("COUNTY_NAME")\nfeature_eng_dict["MEAN_COUNTY_INCOME"] = avg("INCOME").over(county_window_spec)\nfeature_eng_dict["HIGH_INCOME_FLAG"] = (col("INCOME")>col("MEAN_COUNTY_INCOME")).astype(IntegerType())\n\nfeature_eng_dict["AVG_THIRTY_DAY_LOAN_AMOUNT"] =  sql_expr("""AVG(LOAN_AMOUNT) OVER (PARTITION BY COUNTY_NAME ORDER BY TIMESTAMP  \n                                                            RANGE BETWEEN INTERVAL '30 DAYS' PRECEDING AND CURRENT ROW)""")\n\ndf = df.with_columns(feature_eng_dict.keys(), feature_eng_dict.values())\ndf.show(3)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Snowflake Feature Store\u003C/h3\u003E\n","\u003Cp\u003ENow, let's create a Feature Store to track our engineered features:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efs = FeatureStore(\n    session=session, \n    database=DB, \n    name=SCHEMA, \n    default_warehouse=COMPUTE_WAREHOUSE,\n    creation_mode=CreationMode.CREATE_IF_NOT_EXIST\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERegister Entity and Feature View\u003C/h3\u003E\n","\u003Cp\u003EDefine an entity for our loan data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#First try to retrieve an existing entity definition, if not define a new one and register\ntry:\n    #retrieve existing entity\n    loan_id_entity = fs.get_entity('LOAN_ENTITY') \n    print('Retrieved existing entity')\nexcept:\n    #define new entity\n    loan_id_entity = Entity(\n        name = "LOAN_ENTITY",\n        join_keys = ["LOAN_ID"],\n        desc = "Features defined on a per loan level")\n    #register\n    fs.register_entity(loan_id_entity)\n    print("Registered new entity")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a feature view with our engineered features:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Create a dataframe with just the ID, timestamp, and engineered features\nfeature_df = df.select(["LOAN_ID"]+list(feature_eng_dict.keys()))\n\n#define and register feature view\nloan_fv = FeatureView(\n    name="Mortgage_Feature_View",\n    entities=[loan_id_entity],\n    feature_df=feature_df,\n    timestamp_col="TIMESTAMP",\n    refresh_freq="1 day")\n\n#add feature level descriptions\nloan_fv = loan_fv.attach_feature_desc(\n    {\n        "MONTH": "Month of loan",\n        "DAY_OF_YEAR": "Day of calendar year of loan",\n        "DOTW": "Day of the week of loan",\n        "LOAN_AMOUNT": "Loan amount in $USD",\n        "INCOME": "Household income in $USD",\n        "INCOME_LOAN_RATIO": "Ratio of LOAN_AMOUNT/INCOME",\n        "MEAN_COUNTY_INCOME": "Average household income aggregated at county level",\n        "HIGH_INCOME_FLAG": "Binary flag to indicate whether household income is higher than MEAN_COUNTY_INCOME",\n        "AVG_THIRTY_DAY_LOAN_AMOUNT": "Rolling 30 day average of LOAN_AMOUNT"\n    }\n)\n\nloan_fv = fs.register_feature_view(loan_fv, version=VERSION_NUM, overwrite=True)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDataset Generation and Preprocessing\u003C/h2\u003E\n","\u003Ch3\u003EGenerate Dataset from Feature View\u003C/h3\u003E\n","\u003Cp\u003ENow that we have our features registered, let's generate a dataset for model training:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eds = fs.generate_dataset(\n    name=f"MORTGAGE_DATASET_EXTENDED_FEATURES_{VERSION_NUM}",\n    spine_df=df.select("LOAN_ID", "TIMESTAMP", "LOAN_PURPOSE_NAME","MORTGAGERESPONSE"),\n    features=[loan_fv],\n    spine_timestamp_col="TIMESTAMP",\n    spine_label_cols=["MORTGAGERESPONSE"]\n)\n\nds_sp = ds.read.to_snowpark_dataframe()\nds_sp.show(5)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPreprocess Data for Model Training\u003C/h3\u003E\n","\u003Cp\u003ELet's encode categorical variables and prepare our data for training:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport snowflake.ml.modeling.preprocessing as snowml\nfrom snowflake.snowpark.types import StringType\n\nOHE_COLS = ds_sp.select([col.name for col in ds_sp.schema if col.datatype ==StringType()]).columns\nOHE_POST_COLS = [i+"_OHE" for i in OHE_COLS]\n\n# Encode categoricals to numeric columns\nsnowml_ohe = snowml.OneHotEncoder(input_cols=OHE_COLS, output_cols = OHE_COLS, drop_input_cols=True)\nds_sp_ohe = snowml_ohe.fit(ds_sp).transform(ds_sp)\n\n#Rename columns to avoid double nested quotes and white space chars\nrename_dict = {}\nfor i in ds_sp_ohe.columns:\n    if '"' in i:\n        rename_dict[i] = i.replace('"','').replace(' ', '_')\n\nds_sp_ohe = ds_sp_ohe.rename(rename_dict)\n\n# Split data into train and test sets\ntrain, test = ds_sp_ohe.random_split(weights=[0.70, 0.30], seed=0)\ntrain = train.fillna(0)\ntest = test.fillna(0)\n\n# Convert to pandas for model training\ntrain_pd = train.to_pandas()\ntest_pd = test.to_pandas()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBaseline Model Training\u003C/h2\u003E\n","\u003Ch3\u003ETrain a Baseline XGBoost Model\u003C/h3\u003E\n","\u003Cp\u003ELet's train a simple XGBoost classifier as our baseline model:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Define model config\nxgb_base = XGBClassifier(\n    max_depth=50,\n    n_estimators=3,\n    learning_rate = 0.75,\n    booster = 'gbtree')\n\n#Split train data into X, y\nX_train_pd = train_pd.drop(["TIMESTAMP", "LOAN_ID", "MORTGAGERESPONSE"],axis=1)\ny_train_pd = train_pd.MORTGAGERESPONSE\n\n#train model\nxgb_base.fit(X_train_pd,y_train_pd)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEvaluate Baseline Model Performance\u003C/h3\u003E\n","\u003Cp\u003ELet's check how our baseline model performs on the training data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom sklearn.metrics import f1_score, precision_score, recall_score\ntrain_preds_base = xgb_base.predict(X_train_pd)\n\nf1_base_train = round(f1_score(y_train_pd, train_preds_base),4)\nprecision_base_train = round(precision_score(y_train_pd, train_preds_base),4)\nrecall_base_train = round(recall_score(y_train_pd, train_preds_base),4)\n\nprint(f'F1: {f1_base_train} \\nPrecision {precision_base_train} \\nRecall: {recall_base_train}')\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Registry and Evaluation\u003C/h2\u003E\n","\u003Ch3\u003ECreate a Model Registry\u003C/h3\u003E\n","\u003Cp\u003ELet's create a Snowflake Model Registry to track our models:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.registry import Registry\n\n# Define model name\nmodel_name = f"MORTGAGE_LENDING_MLOPS_{VERSION_NUM}"\n\n# Create a registry to log the model to\nmodel_registry = Registry(session=session, \n                          database_name=DB, \n                          schema_name=SCHEMA,\n                          options={"enable_monitoring": True})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERegister Baseline Model\u003C/h3\u003E\n","\u003Cp\u003ENow, let's register our baseline model in the registry:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ebase_version_name = 'XGB_BASE'\n\ntry:\n    mv_base = model_registry.get_model(model_name).version(base_version_name)\n    print("Found existing model version!")\nexcept:\n    print("Logging new model version...")\n    mv_base = model_registry.log_model(\n        model_name=model_name,\n        model=xgb_base, \n        version_name=base_version_name,\n        sample_input_data = train.drop(["TIMESTAMP", "LOAN_ID", "MORTGAGERESPONSE"]).limit(100),\n        comment = """ML model for predicting loan approval likelihood.\n                    This model was trained using xgboost classifier.\n                    Hyperparameters used were:\n                    max_depth=50, n_estimators=3, learning_rate = 0.75, algorithm = gbtree.\n                    """,\n    )\n    mv_base.set_metric(metric_name="Train_F1_Score", value=f1_base_train)\n    mv_base.set_metric(metric_name="Train_Precision_Score", value=precision_base_train)\n    mv_base.set_metric(metric_name="Train_Recall_score", value=recall_base_train)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Production Tag and Apply to Model\u003C/h3\u003E\n","\u003Cp\u003ELet's create a tag for our production model:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Create tag for PROD model\nsession.sql("CREATE OR REPLACE TAG PROD")\n\n#Apply prod tag \nm = model_registry.get_model(model_name)\nm.comment = "Loan approval prediction models" #set model level comment\nm.set_tag("PROD", base_version_name)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEvaluate Baseline Model on Test Data\u003C/h3\u003E\n","\u003Cp\u003ELet's see how our baseline model performs on the test data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ereg_preds = mv_base.run(test, function_name = "predict").rename(col('"output_feature_0"'), "MORTGAGE_PREDICTION")\n\npreds_pd = reg_preds.select(["MORTGAGERESPONSE", "MORTGAGE_PREDICTION"]).to_pandas()\nf1_base_test = round(f1_score(preds_pd.MORTGAGERESPONSE, preds_pd.MORTGAGE_PREDICTION),4)\nprecision_base_test = round(precision_score(preds_pd.MORTGAGERESPONSE, preds_pd.MORTGAGE_PREDICTION),4)\nrecall_base_test = round(recall_score(preds_pd.MORTGAGERESPONSE, preds_pd.MORTGAGE_PREDICTION),4)\n\n#log metrics to model registry model\nmv_base.set_metric(metric_name="Test_F1_Score", value=f1_base_test)\nmv_base.set_metric(metric_name="Test_Precision_Score", value=precision_base_test)\nmv_base.set_metric(metric_name="Test_Recall_score", value=recall_base_test)\n\nprint(f'F1: {f1_base_test} \\nPrecision {precision_base_test} \\nRecall: {recall_base_test}')\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHyperparameter Optimization\u003C/h2\u003E\n","\u003Cp\u003EOur baseline model shows signs of overfitting, with performance dropping significantly from training to test data. Let's use Snowflake's distributed hyperparameter optimization to improve our model.\u003C/p\u003E\n","\u003Ch3\u003ESet Up Hyperparameter Optimization\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003EX_train = train.drop("MORTGAGERESPONSE", "TIMESTAMP", "LOAN_ID")\ny_train = train.select("MORTGAGERESPONSE")\nX_test = test.drop("MORTGAGERESPONSE","TIMESTAMP", "LOAN_ID")\ny_test = test.select("MORTGAGERESPONSE")\n\nfrom snowflake.ml.data import DataConnector\nfrom snowflake.ml.modeling.tune import get_tuner_context\nfrom snowflake.ml.modeling import tune\nfrom entities import search_algorithm\n\n#Define dataset map\ndataset_map = {\n    "x_train": DataConnector.from_dataframe(X_train),\n    "y_train": DataConnector.from_dataframe(y_train),\n    "x_test": DataConnector.from_dataframe(X_test),\n    "y_test": DataConnector.from_dataframe(y_test)\n    }\n\n# Define a training function\ndef train_func():\n    # A context object provided by HPO API to expose data for the current HPO trial\n    tuner_context = get_tuner_context()\n    config = tuner_context.get_hyper_params()\n    dm = tuner_context.get_dataset_map()\n\n    model = XGBClassifier(**config, random_state=42)\n    model.fit(dm["x_train"].to_pandas().sort_index(), dm["y_train"].to_pandas().sort_index())\n    f1_metric = f1_score(\n        dm["y_train"].to_pandas().sort_index(), model.predict(dm["x_train"].to_pandas().sort_index())\n    )\n    tuner_context.report(metrics={"f1_score": f1_metric}, model=model)\n\ntuner = tune.Tuner(\n    train_func=train_func,\n    search_space={\n        "max_depth": tune.randint(1, 10),\n        "learning_rate": tune.uniform(0.01, 0.1),\n        "n_estimators": tune.randint(50, 100),\n    },\n    tuner_config=tune.TunerConfig(\n        metric="f1_score",\n        mode="max",\n        search_alg=search_algorithm.RandomSearch(random_state=101),\n        num_trials=8,\n        max_concurrent_trials=4,\n    ),\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun Hyperparameter Optimization\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Train several model candidates (note this may take 1-2 minutes)\ntuner_results = tuner.run(dataset_map=dataset_map)\n\n#Select best model results and inspect configuration\ntuned_model = tuner_results.best_model\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEvaluate Optimized Model\u003C/h3\u003E\n","\u003Cp\u003ELet's evaluate our optimized model on both training and test data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Generate predictions\nxgb_opt_preds = tuned_model.predict(train_pd.drop(["TIMESTAMP", "LOAN_ID", "MORTGAGERESPONSE"],axis=1))\n\n#Generate performance metrics\nf1_opt_train = round(f1_score(train_pd.MORTGAGERESPONSE, xgb_opt_preds),4)\nprecision_opt_train = round(precision_score(train_pd.MORTGAGERESPONSE, xgb_opt_preds),4)\nrecall_opt_train = round(recall_score(train_pd.MORTGAGERESPONSE, xgb_opt_preds),4)\n\nprint(f'Train Results: \\nF1: {f1_opt_train} \\nPrecision {precision_opt_train} \\nRecall: {recall_opt_train}')\n\n#Generate test predictions\nxgb_opt_preds_test = tuned_model.predict(test_pd.drop(["TIMESTAMP", "LOAN_ID", "MORTGAGERESPONSE"],axis=1))\n\n#Generate performance metrics on test data\nf1_opt_test = round(f1_score(test_pd.MORTGAGERESPONSE, xgb_opt_preds_test),4)\nprecision_opt_test = round(precision_score(test_pd.MORTGAGERESPONSE, xgb_opt_preds_test),4)\nrecall_opt_test = round(recall_score(test_pd.MORTGAGERESPONSE, xgb_opt_preds_test),4)\n\nprint(f'Test Results: \\nF1: {f1_opt_test} \\nPrecision {precision_opt_test} \\nRecall: {recall_opt_test}')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERegister Optimized Model\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Log the optimized model to the model registry\noptimized_version_name = 'XGB_Optimized'\n\ntry:\n    mv_opt = model_registry.get_model(model_name).version(optimized_version_name)\n    print("Found existing model version!")\nexcept:\n    print("Logging new model version...")\n    mv_opt = model_registry.log_model(\n        model_name=model_name,\n        model=tuned_model, \n        version_name=optimized_version_name,\n        sample_input_data = train.drop(["TIMESTAMP", "LOAN_ID", "MORTGAGERESPONSE"]).limit(100),\n        comment = "snow ml model built off feature store using HPO model",\n    )\n    mv_opt.set_metric(metric_name="Train_F1_Score", value=f1_opt_train)\n    mv_opt.set_metric(metric_name="Train_Precision_Score", value=precision_opt_train)\n    mv_opt.set_metric(metric_name="Train_Recall_score", value=recall_opt_train)\n\n    mv_opt.set_metric(metric_name="Test_F1_Score", value=f1_opt_test)\n    mv_opt.set_metric(metric_name="Test_Precision_Score", value=precision_opt_test)\n    mv_opt.set_metric(metric_name="Test_Recall_score", value=recall_opt_test)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUpdate Default Model and Production Tag\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Set the optimized model to be the default model version\nmodel_registry.get_model(model_name).default = optimized_version_name\n\n#Update the PROD tagged model to be the optimized model version\nm.unset_tag("PROD")\nm.set_tag("PROD", optimized_version_name)\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Explainability\u003C/h2\u003E\n","\u003Cp\u003ESnowflake offers built-in explainability capabilities for models logged in the Model Registry. Let's generate SHAP values to understand how input features impact our models' predictions.\u003C/p\u003E\n","\u003Ch3\u003EGenerate SHAP Values\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Create a sample of records for explanation\ntest_pd_sample=test_pd.rename(columns=rename_dict).sample(n=2500, random_state = 100).reset_index(drop=True)\n\n#Compute shapley values for each model\nbase_shap_pd = mv_base.run(test_pd_sample, function_name="explain")\nopt_shap_pd = mv_opt.run(test_pd_sample, function_name="explain")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVisualize Feature Importance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport shap \n\n# Summary plot for base model\nshap.summary_plot(np.array(base_shap_pd.astype(float)), \n                  test_pd_sample.drop(["LOAN_ID","MORTGAGERESPONSE", "TIMESTAMP"], axis=1), \n                  feature_names = test_pd_sample.drop(["LOAN_ID","MORTGAGERESPONSE", "TIMESTAMP"], axis=1).columns)\n\n# Summary plot for optimized model\nshap.summary_plot(np.array(opt_shap_pd.astype(float)), \n                  test_pd_sample.drop(["LOAN_ID","MORTGAGERESPONSE", "TIMESTAMP"], axis=1), \n                  feature_names = test_pd_sample.drop(["LOAN_ID","MORTGAGERESPONSE", "TIMESTAMP"], axis=1).columns)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnalyze Feature Impact\u003C/h3\u003E\n","\u003Cp\u003ELet's analyze how specific features impact our models' predictions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#Merge shap vals and actual vals together for easier plotting\nall_shap_base = test_pd_sample.merge(base_shap_pd, right_index=True, left_index=True, how='outer')\nall_shap_opt = test_pd_sample.merge(opt_shap_pd, right_index=True, left_index=True, how='outer')\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Analyze income impact\nasb_filtered = all_shap_base[(all_shap_base.INCOME>0) & (all_shap_base.INCOME<250000)]\naso_filtered = all_shap_opt[(all_shap_opt.INCOME>0) & (all_shap_opt.INCOME<250000)]\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 6))\nfig.suptitle("INCOME EXPLANATION")\nsns.scatterplot(data = asb_filtered, x ='INCOME', y = 'INCOME_explanation', ax=axes[0])\nsns.regplot(data = asb_filtered, x ="INCOME", y = 'INCOME_explanation', scatter=False, color='red', line_kws={"lw":2},ci =100, lowess=False, ax =axes[0])\naxes[0].set_title('Base Model')\nsns.scatterplot(data = aso_filtered, x ='INCOME', y = 'INCOME_explanation',color = "orange", ax = axes[1])\nsns.regplot(data = aso_filtered, x ="INCOME", y = 'INCOME_explanation', scatter=False, color='blue', line_kws={"lw":2},ci =100, lowess=False, ax =axes[1])\naxes[1].set_title('Opt Model')\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Monitoring Setup\u003C/h2\u003E\n","\u003Cp\u003ELet's set up model monitoring to track our models' performance over time.\u003C/p\u003E\n","\u003Ch3\u003ESave Training and Test Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrain.write.save_as_table(f"DEMO_MORTGAGE_LENDING_TRAIN_{VERSION_NUM}", mode="overwrite")\ntest.write.save_as_table(f"DEMO_MORTGAGE_LENDING_TEST_{VERSION_NUM}", mode="overwrite")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Inference Stored Procedure\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake import snowpark\n\ndef demo_inference_sproc(session: snowpark.Session, table_name: str, modelname: str, modelversion: str) -> str:\n    reg = Registry(session=session)\n    m = reg.get_model(model_name)\n    mv = m.version(modelversion)\n    \n    input_table_name=table_name\n    pred_col = f'{modelversion}_PREDICTION'\n\n    # Read the input table to a dataframe\n    df = session.table(input_table_name)\n    results = mv.run(df, function_name="predict").select("LOAN_ID",'"output_feature_0"').withColumnRenamed('"output_feature_0"', pred_col)\n\n    final = df.join(results, on="LOAN_ID", how="full")\n    # Write results back to Snowflake table\n    final.write.save_as_table(table_name, mode='overwrite',enable_schema_evolution=True)\n\n    return "Success"\n\n# Register the stored procedure\nsession.sproc.register(\n    func=demo_inference_sproc,\n    name="model_inference_sproc",\n    replace=True,\n    is_permanent=True,\n    stage_location="@ML_STAGE",\n    packages=['joblib', 'snowflake-snowpark-python', 'snowflake-ml-python'],\n    return_type=StringType()\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun Inference on Training and Test Data\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TRAIN_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_BASE');\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TEST_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_BASE');\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TRAIN_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_OPTIMIZED');\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TEST_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_OPTIMIZED');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Model Monitors\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE MODEL MONITOR MORTGAGE_LENDING_BASE_MODEL_MONITOR\nWITH\n    MODEL=MORTGAGE_LENDING_MLOPS_0\n    VERSION=XGB_BASE\n    FUNCTION=predict\n    SOURCE=DEMO_MORTGAGE_LENDING_TEST_0\n    BASELINE=DEMO_MORTGAGE_LENDING_TRAIN_0\n    TIMESTAMP_COLUMN=TIMESTAMP\n    PREDICTION_CLASS_COLUMNS=(XGB_BASE_PREDICTION)  \n    ACTUAL_CLASS_COLUMNS=(MORTGAGERESPONSE)\n    ID_COLUMNS=(LOAN_ID)\n    WAREHOUSE=E2E_SNOW_MLOPS_WH\n    REFRESH_INTERVAL='1 hour'\n    AGGREGATION_WINDOW='1 day';\n\nCREATE OR REPLACE MODEL MONITOR MORTGAGE_LENDING_OPTIMIZED_MODEL_MONITOR\nWITH\n    MODEL=MORTGAGE_LENDING_MLOPS_0\n    VERSION=XGB_OPTIMIZED\n    FUNCTION=predict\n    SOURCE=DEMO_MORTGAGE_LENDING_TEST_0\n    BASELINE=DEMO_MORTGAGE_LENDING_TRAIN_0\n    TIMESTAMP_COLUMN=TIMESTAMP\n    PREDICTION_CLASS_COLUMNS=(XGB_OPTIMIZED_PREDICTION)  \n    ACTUAL_CLASS_COLUMNS=(MORTGAGERESPONSE)\n    ID_COLUMNS=(LOAN_ID)\n    WAREHOUSE=E2E_SNOW_MLOPS_WH\n    REFRESH_INTERVAL='12 hours'\n    AGGREGATION_WINDOW='1 day';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EQuery Model Drift Metrics\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(MODEL_MONITOR_DRIFT_METRIC(\n'MORTGAGE_LENDING_BASE_MODEL_MONITOR', -- model monitor to use\n'DIFFERENCE_OF_MEANS', -- metric for computing drift\n'XGB_BASE_PREDICTION', -- column to compute drift on\n'1 DAY',  -- day granularity for drift computation\nDATEADD(DAY, -90, CURRENT_DATE()), -- end date\nDATEADD(DAY, -60, CURRENT_DATE()) -- start date\n))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou just walked through a guided experience building and deploying a complete end-to-end machine learning workflow within \u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML\u003C/a\u003E for a mortgage lending prediction case. The workflow covers feature engineering with Snowflake Feature Store, model training and hyperparameter optimization using Snowflake ML APIs, model logging and management with Snowflake Model Registry, and model performance tracking and drift detection via ML Observability.\u003C/p\u003E\n","\u003Cp\u003EReady for more? After you complete this quickstart, you can try another guided ML example \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ERelated Resources\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003ESnowflake ML Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML Resources\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/best-practices-for-production-ml/\"\u003EBest Practices for Production ML\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/agentic-ml-best-practices-cortex-code/\"\u003EAgentic Machine Learning Best Practices with Snowflake CoCo\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-end-to-end-ml-workflow-in-snowflake/blob/main/train_deploy_monitor_ML_in_snowflake.ipynb?_fsi=hnlih63N&_fsi=hnlih63N\"\u003EFork Notebook on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2025/quickstart-reference-architecture.pdf\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build complete ML workflows in Snowflake from data preparation through training, evaluation, deployment, monitoring, and iteration.","title":"Build an End-to-End ML Workflow in Snowflake","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nIn this guide, you'll learn how to build and deploy a complete machine learning workflow entirely within [Snowflake ML](http://www.snowflake.com/ml). You'll work through a mortgage lending prediction use case, implementing each stage of the ML lifecycle from feature engineering to model deployment and monitoring.\n\nThis tutorial showcases Snowflake's ML capabilities, including:\n- Feature Store for defining and managing features\n- Snowflake ML APIs for model training and hyperparameter optimization\n- Model Registry for versioning and lifecycle management\n- ML Observability for tracking performance and drift\n\n### What You'll Learn\n- How to build a model in Snowflake Notebooks on Container Runtime \n- How to deploy a model for inference seamlessly with Snowflake Model Registry\n- How to monitor a model during production with ML Observability\n\n### What You'll Build\nYou'll build a complete mortgage lending prediction system that:\n1. Engineers features from loan application data\n2. Trains baseline and optimized XGBoost models\n3. Registers models with metadata and metrics\n4. Monitors model performance over time\n5. Provides model explanations using SHAP values\n\n### What You'll Need\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with ACCOUNTADMIN access.  Sign up for a 30-day free trial account, if required.\n- Basic understanding of Python and machine learning concepts\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\nFirstly, run this SQL setup script to create the necessary objects:\n```sql\n-- Using ACCOUNTADMIN, create a new role for this exercise \nUSE ROLE ACCOUNTADMIN;\nSET USERNAME = (SELECT CURRENT_USER());\nSET ALLOW_EXTERNAL_ACCESS_FOR_TRIAL_ACCOUNTS = TRUE;\nCREATE OR REPLACE ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- Grant necessary permissions to create databases, compute pools, and service endpoints to new role\nGRANT CREATE DATABASE on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE; \nGRANT CREATE COMPUTE POOL on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\nGRANT CREATE WAREHOUSE ON ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\nGRANT BIND SERVICE ENDPOINT on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- grant new role to user and switch to that role\nGRANT ROLE E2E_SNOW_MLOPS_ROLE to USER identifier($USERNAME);\nUSE ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- Create warehouse\nCREATE OR REPLACE WAREHOUSE E2E_SNOW_MLOPS_WH WITH WAREHOUSE_SIZE='MEDIUM';\n\n-- Create Database \nCREATE OR REPLACE DATABASE E2E_SNOW_MLOPS_DB;\n\n-- Create Schema\nCREATE OR REPLACE SCHEMA MLOPS_SCHEMA;\n\n-- Create compute pool\nCREATE COMPUTE POOL IF NOT EXISTS MLOPS_COMPUTE_POOL \n MIN_NODES = 1\n MAX_NODES = 3\n INSTANCE_FAMILY = CPU_X64_M;\n\n-- Using accountadmin, grant privilege to create network rules and integrations on newly created db\nUSE ROLE ACCOUNTADMIN;\n-- GRANT CREATE NETWORK RULE on SCHEMA MLOPS_SCHEMA to ROLE E2E_SNOW_MLOPS_ROLE;\nGRANT CREATE INTEGRATION on ACCOUNT to ROLE E2E_SNOW_MLOPS_ROLE;\nUSE ROLE E2E_SNOW_MLOPS_ROLE;\n\n-- Create an API integration with Github\nCREATE OR REPLACE API INTEGRATION GITHUB_INTEGRATION_E2E_SNOW_MLOPS\n   api_provider = git_https_api\n   api_allowed_prefixes = ('https://github.com/Snowflake-Labs')\n   API_USER_AUTHENTICATION = (TYPE = SNOWFLAKE_GITHUB_APP)\n   enabled = true\n   comment='Git integration with Snowflake Demo Github Repository.';\n\n```\n\n### Create workspace\nNow we can navigate to the Workspaces tab in Snowsight to create a git based workspace! \n\nFrom the workspace drop down, choose create from Git repository:\n\n![git-workspace1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/workspace_setup1.png?v=89cbbeb6)\n\nCreate the workspace based on the repository url https://github.com/Snowflake-Labs/sfguide-build-end-to-end-ml-workflow-in-snowflake.git\n\n![git-workspace2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/workspace_setup2.png?v=89cbbeb6)\n\nNext, create a service to run the notebook on the compute pool created by the setup:\n\n![create-service1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/create_service1.png?v=89cbbeb6)\n\n![create-service2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/end-to-end-ml-workflow/create_service2.png?v=89cbbeb6)\n\nBe sure to run this with the newly created role **E2E_SNOW_MLOPS_ROLE** and warehouse **E2E_SNOW_MLOPS_WH**!\n\n### Initialize Snowflake Session and Variables\n\nSet up your environment variables and initialize your Snowflake session:\n\n```python\n#Update this VERSION_NUM to version your features, models etc!\nVERSION_NUM = '0'\nDB = \"E2E_SNOW_MLOPS_DB\" \nSCHEMA = \"MLOPS_SCHEMA\" \nCOMPUTE_WAREHOUSE = \"E2E_SNOW_MLOPS_WH\" \n\nimport pandas as pd\nimport numpy as np\nimport sklearn\nimport math\nimport pickle\nimport shap\nfrom datetime import datetime\nimport streamlit as st\nfrom xgboost import XGBClassifier\n\n# Snowflake ML\nfrom snowflake.ml.registry import Registry\nfrom snowflake.ml.modeling.tune import get_tuner_context\nfrom snowflake.ml.modeling import tune\nfrom entities import search_algorithm\n\n#Snowflake feature store\nfrom snowflake.ml.feature_store import FeatureStore, FeatureView, Entity, CreationMode\n\n# Snowpark session\nfrom snowflake.snowpark import DataFrame\nfrom snowflake.snowpark.functions import col, to_timestamp, min, max, month, dayofweek, dayofyear, avg, date_add, sql_expr\nfrom snowflake.snowpark.types import IntegerType\nfrom snowflake.snowpark import Window\n\n#setup snowpark session\nfrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n```\n\n### Load and Prepare Data\n\nLoad the mortgage lending demo data:\n\n```python\ntry:\n    print(\"Reading table data...\")\n    df = session.table(\"MORTGAGE_LENDING_DEMO_DATA\")\n    df.show(5)\nexcept:\n    print(\"Table not found! Uploading data to snowflake table\")\n    df_pandas = pd.read_csv(\"MORTGAGE_LENDING_DEMO_DATA.csv.zip\")\n    session.write_pandas(df_pandas, \"MORTGAGE_LENDING_DEMO_DATA\", auto_create_table=True)\n    df = session.table(\"MORTGAGE_LENDING_DEMO_DATA\")\n    df.show(5)\n```\n\n\u003E \n\u003E IMPORTANT:\n\u003E - Make sure your Snowflake account has the necessary privileges to create tables and execute ML operations\n\u003E - Ensure your warehouse is properly sized for ML workloads\n\n\u003C!-- ------------------------ --\u003E\n## Feature Engineering\n\nIn this section, we'll create features from our raw mortgage lending data using Snowpark APIs.\n\n### Create Feature Transformations\n\nFirst, let's examine the time range of our data:\n\n```python\ndf.select(min('TS'), max('TS'))\n```\n\nNow, let's create a dictionary of feature transformations:\n\n```python\n#Create a dict with keys for feature names and values containing transform code\nfeature_eng_dict = dict()\n\n#Get current date and time\ncurrent_time = datetime.now()\ndf_max_time = datetime.strptime(str(df.select(max(\"TS\")).collect()[0][0]), \"%Y-%m-%d %H:%M:%S.%f\")\n\n#Find delta between latest existing timestamp and today's date\ntimedelta = current_time- df_max_time\n\n#Timstamp features\nfeature_eng_dict[\"TIMESTAMP\"] = date_add(to_timestamp(\"TS\"), timedelta.days-1)\nfeature_eng_dict[\"MONTH\"] = month(\"TIMESTAMP\")\nfeature_eng_dict[\"DAY_OF_YEAR\"] = dayofyear(\"TIMESTAMP\") \nfeature_eng_dict[\"DOTW\"] = dayofweek(\"TIMESTAMP\")\n\n#Income and loan features\nfeature_eng_dict[\"LOAN_AMOUNT\"] = col(\"LOAN_AMOUNT_000s\")*1000\nfeature_eng_dict[\"INCOME\"] = col(\"APPLICANT_INCOME_000s\")*1000\nfeature_eng_dict[\"INCOME_LOAN_RATIO\"] = col(\"INCOME\")/col(\"LOAN_AMOUNT\")\n\ncounty_window_spec = Window.partition_by(\"COUNTY_NAME\")\nfeature_eng_dict[\"MEAN_COUNTY_INCOME\"] = avg(\"INCOME\").over(county_window_spec)\nfeature_eng_dict[\"HIGH_INCOME_FLAG\"] = (col(\"INCOME\")\u003Ecol(\"MEAN_COUNTY_INCOME\")).astype(IntegerType())\n\nfeature_eng_dict[\"AVG_THIRTY_DAY_LOAN_AMOUNT\"] =  sql_expr(\"\"\"AVG(LOAN_AMOUNT) OVER (PARTITION BY COUNTY_NAME ORDER BY TIMESTAMP  \n                                                            RANGE BETWEEN INTERVAL '30 DAYS' PRECEDING AND CURRENT ROW)\"\"\")\n\ndf = df.with_columns(feature_eng_dict.keys(), feature_eng_dict.values())\ndf.show(3)\n```\n\n### Create a Snowflake Feature Store\n\nNow, let's create a Feature Store to track our engineered features:\n\n```python\nfs = FeatureStore(\n    session=session, \n    database=DB, \n    name=SCHEMA, \n    default_warehouse=COMPUTE_WAREHOUSE,\n    creation_mode=CreationMode.CREATE_IF_NOT_EXIST\n)\n```\n\n### Register Entity and Feature View\n\nDefine an entity for our loan data:\n\n```python\n#First try to retrieve an existing entity definition, if not define a new one and register\ntry:\n    #retrieve existing entity\n    loan_id_entity = fs.get_entity('LOAN_ENTITY') \n    print('Retrieved existing entity')\nexcept:\n    #define new entity\n    loan_id_entity = Entity(\n        name = \"LOAN_ENTITY\",\n        join_keys = [\"LOAN_ID\"],\n        desc = \"Features defined on a per loan level\")\n    #register\n    fs.register_entity(loan_id_entity)\n    print(\"Registered new entity\")\n```\n\nCreate a feature view with our engineered features:\n\n```python\n#Create a dataframe with just the ID, timestamp, and engineered features\nfeature_df = df.select([\"LOAN_ID\"]+list(feature_eng_dict.keys()))\n\n#define and register feature view\nloan_fv = FeatureView(\n    name=\"Mortgage_Feature_View\",\n    entities=[loan_id_entity],\n    feature_df=feature_df,\n    timestamp_col=\"TIMESTAMP\",\n    refresh_freq=\"1 day\")\n\n#add feature level descriptions\nloan_fv = loan_fv.attach_feature_desc(\n    {\n        \"MONTH\": \"Month of loan\",\n        \"DAY_OF_YEAR\": \"Day of calendar year of loan\",\n        \"DOTW\": \"Day of the week of loan\",\n        \"LOAN_AMOUNT\": \"Loan amount in $USD\",\n        \"INCOME\": \"Household income in $USD\",\n        \"INCOME_LOAN_RATIO\": \"Ratio of LOAN_AMOUNT/INCOME\",\n        \"MEAN_COUNTY_INCOME\": \"Average household income aggregated at county level\",\n        \"HIGH_INCOME_FLAG\": \"Binary flag to indicate whether household income is higher than MEAN_COUNTY_INCOME\",\n        \"AVG_THIRTY_DAY_LOAN_AMOUNT\": \"Rolling 30 day average of LOAN_AMOUNT\"\n    }\n)\n\nloan_fv = fs.register_feature_view(loan_fv, version=VERSION_NUM, overwrite=True)\n```\n\n\u003C!-- ------------------------ --\u003E\n## Dataset Generation and Preprocessing\n\n### Generate Dataset from Feature View\n\nNow that we have our features registered, let's generate a dataset for model training:\n\n```python\nds = fs.generate_dataset(\n    name=f\"MORTGAGE_DATASET_EXTENDED_FEATURES_{VERSION_NUM}\",\n    spine_df=df.select(\"LOAN_ID\", \"TIMESTAMP\", \"LOAN_PURPOSE_NAME\",\"MORTGAGERESPONSE\"),\n    features=[loan_fv],\n    spine_timestamp_col=\"TIMESTAMP\",\n    spine_label_cols=[\"MORTGAGERESPONSE\"]\n)\n\nds_sp = ds.read.to_snowpark_dataframe()\nds_sp.show(5)\n```\n\n### Preprocess Data for Model Training\n\nLet's encode categorical variables and prepare our data for training:\n\n```python\nimport snowflake.ml.modeling.preprocessing as snowml\nfrom snowflake.snowpark.types import StringType\n\nOHE_COLS = ds_sp.select([col.name for col in ds_sp.schema if col.datatype ==StringType()]).columns\nOHE_POST_COLS = [i+\"_OHE\" for i in OHE_COLS]\n\n# Encode categoricals to numeric columns\nsnowml_ohe = snowml.OneHotEncoder(input_cols=OHE_COLS, output_cols = OHE_COLS, drop_input_cols=True)\nds_sp_ohe = snowml_ohe.fit(ds_sp).transform(ds_sp)\n\n#Rename columns to avoid double nested quotes and white space chars\nrename_dict = {}\nfor i in ds_sp_ohe.columns:\n    if '\"' in i:\n        rename_dict[i] = i.replace('\"','').replace(' ', '_')\n\nds_sp_ohe = ds_sp_ohe.rename(rename_dict)\n\n# Split data into train and test sets\ntrain, test = ds_sp_ohe.random_split(weights=[0.70, 0.30], seed=0)\ntrain = train.fillna(0)\ntest = test.fillna(0)\n\n# Convert to pandas for model training\ntrain_pd = train.to_pandas()\ntest_pd = test.to_pandas()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Baseline Model Training\n\n### Train a Baseline XGBoost Model\n\nLet's train a simple XGBoost classifier as our baseline model:\n\n```python\n#Define model config\nxgb_base = XGBClassifier(\n    max_depth=50,\n    n_estimators=3,\n    learning_rate = 0.75,\n    booster = 'gbtree')\n\n#Split train data into X, y\nX_train_pd = train_pd.drop([\"TIMESTAMP\", \"LOAN_ID\", \"MORTGAGERESPONSE\"],axis=1)\ny_train_pd = train_pd.MORTGAGERESPONSE\n\n#train model\nxgb_base.fit(X_train_pd,y_train_pd)\n```\n\n### Evaluate Baseline Model Performance\n\nLet's check how our baseline model performs on the training data:\n\n```python\nfrom sklearn.metrics import f1_score, precision_score, recall_score\ntrain_preds_base = xgb_base.predict(X_train_pd)\n\nf1_base_train = round(f1_score(y_train_pd, train_preds_base),4)\nprecision_base_train = round(precision_score(y_train_pd, train_preds_base),4)\nrecall_base_train = round(recall_score(y_train_pd, train_preds_base),4)\n\nprint(f'F1: {f1_base_train} \\nPrecision {precision_base_train} \\nRecall: {recall_base_train}')\n```\n\n\u003C!-- ------------------------ --\u003E\n## Model Registry and Evaluation\n\n### Create a Model Registry\n\nLet's create a Snowflake Model Registry to track our models:\n\n```python\nfrom snowflake.ml.registry import Registry\n\n# Define model name\nmodel_name = f\"MORTGAGE_LENDING_MLOPS_{VERSION_NUM}\"\n\n# Create a registry to log the model to\nmodel_registry = Registry(session=session, \n                          database_name=DB, \n                          schema_name=SCHEMA,\n                          options={\"enable_monitoring\": True})\n```\n\n### Register Baseline Model\n\nNow, let's register our baseline model in the registry:\n\n```python\nbase_version_name = 'XGB_BASE'\n\ntry:\n    mv_base = model_registry.get_model(model_name).version(base_version_name)\n    print(\"Found existing model version!\")\nexcept:\n    print(\"Logging new model version...\")\n    mv_base = model_registry.log_model(\n        model_name=model_name,\n        model=xgb_base, \n        version_name=base_version_name,\n        sample_input_data = train.drop([\"TIMESTAMP\", \"LOAN_ID\", \"MORTGAGERESPONSE\"]).limit(100),\n        comment = \"\"\"ML model for predicting loan approval likelihood.\n                    This model was trained using xgboost classifier.\n                    Hyperparameters used were:\n                    max_depth=50, n_estimators=3, learning_rate = 0.75, algorithm = gbtree.\n                    \"\"\",\n    )\n    mv_base.set_metric(metric_name=\"Train_F1_Score\", value=f1_base_train)\n    mv_base.set_metric(metric_name=\"Train_Precision_Score\", value=precision_base_train)\n    mv_base.set_metric(metric_name=\"Train_Recall_score\", value=recall_base_train)\n```\n\n### Create Production Tag and Apply to Model\n\nLet's create a tag for our production model:\n\n```python\n#Create tag for PROD model\nsession.sql(\"CREATE OR REPLACE TAG PROD\")\n\n#Apply prod tag \nm = model_registry.get_model(model_name)\nm.comment = \"Loan approval prediction models\" #set model level comment\nm.set_tag(\"PROD\", base_version_name)\n```\n\n### Evaluate Baseline Model on Test Data\n\nLet's see how our baseline model performs on the test data:\n\n```python\nreg_preds = mv_base.run(test, function_name = \"predict\").rename(col('\"output_feature_0\"'), \"MORTGAGE_PREDICTION\")\n\npreds_pd = reg_preds.select([\"MORTGAGERESPONSE\", \"MORTGAGE_PREDICTION\"]).to_pandas()\nf1_base_test = round(f1_score(preds_pd.MORTGAGERESPONSE, preds_pd.MORTGAGE_PREDICTION),4)\nprecision_base_test = round(precision_score(preds_pd.MORTGAGERESPONSE, preds_pd.MORTGAGE_PREDICTION),4)\nrecall_base_test = round(recall_score(preds_pd.MORTGAGERESPONSE, preds_pd.MORTGAGE_PREDICTION),4)\n\n#log metrics to model registry model\nmv_base.set_metric(metric_name=\"Test_F1_Score\", value=f1_base_test)\nmv_base.set_metric(metric_name=\"Test_Precision_Score\", value=precision_base_test)\nmv_base.set_metric(metric_name=\"Test_Recall_score\", value=recall_base_test)\n\nprint(f'F1: {f1_base_test} \\nPrecision {precision_base_test} \\nRecall: {recall_base_test}')\n```\n\n\u003C!-- ------------------------ --\u003E\n## Hyperparameter Optimization\n\nOur baseline model shows signs of overfitting, with performance dropping significantly from training to test data. Let's use Snowflake's distributed hyperparameter optimization to improve our model.\n\n### Set Up Hyperparameter Optimization\n\n```python\nX_train = train.drop(\"MORTGAGERESPONSE\", \"TIMESTAMP\", \"LOAN_ID\")\ny_train = train.select(\"MORTGAGERESPONSE\")\nX_test = test.drop(\"MORTGAGERESPONSE\",\"TIMESTAMP\", \"LOAN_ID\")\ny_test = test.select(\"MORTGAGERESPONSE\")\n\nfrom snowflake.ml.data import DataConnector\nfrom snowflake.ml.modeling.tune import get_tuner_context\nfrom snowflake.ml.modeling import tune\nfrom entities import search_algorithm\n\n#Define dataset map\ndataset_map = {\n    \"x_train\": DataConnector.from_dataframe(X_train),\n    \"y_train\": DataConnector.from_dataframe(y_train),\n    \"x_test\": DataConnector.from_dataframe(X_test),\n    \"y_test\": DataConnector.from_dataframe(y_test)\n    }\n\n# Define a training function\ndef train_func():\n    # A context object provided by HPO API to expose data for the current HPO trial\n    tuner_context = get_tuner_context()\n    config = tuner_context.get_hyper_params()\n    dm = tuner_context.get_dataset_map()\n\n    model = XGBClassifier(**config, random_state=42)\n    model.fit(dm[\"x_train\"].to_pandas().sort_index(), dm[\"y_train\"].to_pandas().sort_index())\n    f1_metric = f1_score(\n        dm[\"y_train\"].to_pandas().sort_index(), model.predict(dm[\"x_train\"].to_pandas().sort_index())\n    )\n    tuner_context.report(metrics={\"f1_score\": f1_metric}, model=model)\n\ntuner = tune.Tuner(\n    train_func=train_func,\n    search_space={\n        \"max_depth\": tune.randint(1, 10),\n        \"learning_rate\": tune.uniform(0.01, 0.1),\n        \"n_estimators\": tune.randint(50, 100),\n    },\n    tuner_config=tune.TunerConfig(\n        metric=\"f1_score\",\n        mode=\"max\",\n        search_alg=search_algorithm.RandomSearch(random_state=101),\n        num_trials=8,\n        max_concurrent_trials=4,\n    ),\n)\n```\n\n### Run Hyperparameter Optimization\n\n```python\n#Train several model candidates (note this may take 1-2 minutes)\ntuner_results = tuner.run(dataset_map=dataset_map)\n\n#Select best model results and inspect configuration\ntuned_model = tuner_results.best_model\n```\n\n### Evaluate Optimized Model\n\nLet's evaluate our optimized model on both training and test data:\n\n```python\n#Generate predictions\nxgb_opt_preds = tuned_model.predict(train_pd.drop([\"TIMESTAMP\", \"LOAN_ID\", \"MORTGAGERESPONSE\"],axis=1))\n\n#Generate performance metrics\nf1_opt_train = round(f1_score(train_pd.MORTGAGERESPONSE, xgb_opt_preds),4)\nprecision_opt_train = round(precision_score(train_pd.MORTGAGERESPONSE, xgb_opt_preds),4)\nrecall_opt_train = round(recall_score(train_pd.MORTGAGERESPONSE, xgb_opt_preds),4)\n\nprint(f'Train Results: \\nF1: {f1_opt_train} \\nPrecision {precision_opt_train} \\nRecall: {recall_opt_train}')\n\n#Generate test predictions\nxgb_opt_preds_test = tuned_model.predict(test_pd.drop([\"TIMESTAMP\", \"LOAN_ID\", \"MORTGAGERESPONSE\"],axis=1))\n\n#Generate performance metrics on test data\nf1_opt_test = round(f1_score(test_pd.MORTGAGERESPONSE, xgb_opt_preds_test),4)\nprecision_opt_test = round(precision_score(test_pd.MORTGAGERESPONSE, xgb_opt_preds_test),4)\nrecall_opt_test = round(recall_score(test_pd.MORTGAGERESPONSE, xgb_opt_preds_test),4)\n\nprint(f'Test Results: \\nF1: {f1_opt_test} \\nPrecision {precision_opt_test} \\nRecall: {recall_opt_test}')\n```\n\n### Register Optimized Model\n\n```python\n#Log the optimized model to the model registry\noptimized_version_name = 'XGB_Optimized'\n\ntry:\n    mv_opt = model_registry.get_model(model_name).version(optimized_version_name)\n    print(\"Found existing model version!\")\nexcept:\n    print(\"Logging new model version...\")\n    mv_opt = model_registry.log_model(\n        model_name=model_name,\n        model=tuned_model, \n        version_name=optimized_version_name,\n        sample_input_data = train.drop([\"TIMESTAMP\", \"LOAN_ID\", \"MORTGAGERESPONSE\"]).limit(100),\n        comment = \"snow ml model built off feature store using HPO model\",\n    )\n    mv_opt.set_metric(metric_name=\"Train_F1_Score\", value=f1_opt_train)\n    mv_opt.set_metric(metric_name=\"Train_Precision_Score\", value=precision_opt_train)\n    mv_opt.set_metric(metric_name=\"Train_Recall_score\", value=recall_opt_train)\n\n    mv_opt.set_metric(metric_name=\"Test_F1_Score\", value=f1_opt_test)\n    mv_opt.set_metric(metric_name=\"Test_Precision_Score\", value=precision_opt_test)\n    mv_opt.set_metric(metric_name=\"Test_Recall_score\", value=recall_opt_test)\n```\n\n### Update Default Model and Production Tag\n\n```python\n#Set the optimized model to be the default model version\nmodel_registry.get_model(model_name).default = optimized_version_name\n\n#Update the PROD tagged model to be the optimized model version\nm.unset_tag(\"PROD\")\nm.set_tag(\"PROD\", optimized_version_name)\n```\n\n\u003C!-- ------------------------ --\u003E\n## Model Explainability\n\nSnowflake offers built-in explainability capabilities for models logged in the Model Registry. Let's generate SHAP values to understand how input features impact our models' predictions.\n\n### Generate SHAP Values\n\n```python\n#Create a sample of records for explanation\ntest_pd_sample=test_pd.rename(columns=rename_dict).sample(n=2500, random_state = 100).reset_index(drop=True)\n\n#Compute shapley values for each model\nbase_shap_pd = mv_base.run(test_pd_sample, function_name=\"explain\")\nopt_shap_pd = mv_opt.run(test_pd_sample, function_name=\"explain\")\n```\n\n### Visualize Feature Importance\n\n```python\nimport shap \n\n# Summary plot for base model\nshap.summary_plot(np.array(base_shap_pd.astype(float)), \n                  test_pd_sample.drop([\"LOAN_ID\",\"MORTGAGERESPONSE\", \"TIMESTAMP\"], axis=1), \n                  feature_names = test_pd_sample.drop([\"LOAN_ID\",\"MORTGAGERESPONSE\", \"TIMESTAMP\"], axis=1).columns)\n\n# Summary plot for optimized model\nshap.summary_plot(np.array(opt_shap_pd.astype(float)), \n                  test_pd_sample.drop([\"LOAN_ID\",\"MORTGAGERESPONSE\", \"TIMESTAMP\"], axis=1), \n                  feature_names = test_pd_sample.drop([\"LOAN_ID\",\"MORTGAGERESPONSE\", \"TIMESTAMP\"], axis=1).columns)\n```\n\n### Analyze Feature Impact\n\nLet's analyze how specific features impact our models' predictions:\n\n```python\n#Merge shap vals and actual vals together for easier plotting\nall_shap_base = test_pd_sample.merge(base_shap_pd, right_index=True, left_index=True, how='outer')\nall_shap_opt = test_pd_sample.merge(opt_shap_pd, right_index=True, left_index=True, how='outer')\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Analyze income impact\nasb_filtered = all_shap_base[(all_shap_base.INCOME\u003E0) & (all_shap_base.INCOME\u003C250000)]\naso_filtered = all_shap_opt[(all_shap_opt.INCOME\u003E0) & (all_shap_opt.INCOME\u003C250000)]\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 6))\nfig.suptitle(\"INCOME EXPLANATION\")\nsns.scatterplot(data = asb_filtered, x ='INCOME', y = 'INCOME_explanation', ax=axes[0])\nsns.regplot(data = asb_filtered, x =\"INCOME\", y = 'INCOME_explanation', scatter=False, color='red', line_kws={\"lw\":2},ci =100, lowess=False, ax =axes[0])\naxes[0].set_title('Base Model')\nsns.scatterplot(data = aso_filtered, x ='INCOME', y = 'INCOME_explanation',color = \"orange\", ax = axes[1])\nsns.regplot(data = aso_filtered, x =\"INCOME\", y = 'INCOME_explanation', scatter=False, color='blue', line_kws={\"lw\":2},ci =100, lowess=False, ax =axes[1])\naxes[1].set_title('Opt Model')\nplt.show()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Model Monitoring Setup\n\nLet's set up model monitoring to track our models' performance over time.\n\n### Save Training and Test Data\n\n```python\ntrain.write.save_as_table(f\"DEMO_MORTGAGE_LENDING_TRAIN_{VERSION_NUM}\", mode=\"overwrite\")\ntest.write.save_as_table(f\"DEMO_MORTGAGE_LENDING_TEST_{VERSION_NUM}\", mode=\"overwrite\")\n```\n\n### Create Inference Stored Procedure\n\n```python\nfrom snowflake import snowpark\n\ndef demo_inference_sproc(session: snowpark.Session, table_name: str, modelname: str, modelversion: str) -\u003E str:\n    reg = Registry(session=session)\n    m = reg.get_model(model_name)\n    mv = m.version(modelversion)\n    \n    input_table_name=table_name\n    pred_col = f'{modelversion}_PREDICTION'\n\n    # Read the input table to a dataframe\n    df = session.table(input_table_name)\n    results = mv.run(df, function_name=\"predict\").select(\"LOAN_ID\",'\"output_feature_0\"').withColumnRenamed('\"output_feature_0\"', pred_col)\n\n    final = df.join(results, on=\"LOAN_ID\", how=\"full\")\n    # Write results back to Snowflake table\n    final.write.save_as_table(table_name, mode='overwrite',enable_schema_evolution=True)\n\n    return \"Success\"\n\n# Register the stored procedure\nsession.sproc.register(\n    func=demo_inference_sproc,\n    name=\"model_inference_sproc\",\n    replace=True,\n    is_permanent=True,\n    stage_location=\"@ML_STAGE\",\n    packages=['joblib', 'snowflake-snowpark-python', 'snowflake-ml-python'],\n    return_type=StringType()\n)\n```\n\n### Run Inference on Training and Test Data\n\n```sql\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TRAIN_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_BASE');\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TEST_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_BASE');\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TRAIN_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_OPTIMIZED');\nCALL model_inference_sproc('DEMO_MORTGAGE_LENDING_TEST_0','MORTGAGE_LENDING_MLOPS_0', 'XGB_OPTIMIZED');\n```\n\n### Create Model Monitors\n\n```sql\nCREATE OR REPLACE MODEL MONITOR MORTGAGE_LENDING_BASE_MODEL_MONITOR\nWITH\n    MODEL=MORTGAGE_LENDING_MLOPS_0\n    VERSION=XGB_BASE\n    FUNCTION=predict\n    SOURCE=DEMO_MORTGAGE_LENDING_TEST_0\n    BASELINE=DEMO_MORTGAGE_LENDING_TRAIN_0\n    TIMESTAMP_COLUMN=TIMESTAMP\n    PREDICTION_CLASS_COLUMNS=(XGB_BASE_PREDICTION)  \n    ACTUAL_CLASS_COLUMNS=(MORTGAGERESPONSE)\n    ID_COLUMNS=(LOAN_ID)\n    WAREHOUSE=E2E_SNOW_MLOPS_WH\n    REFRESH_INTERVAL='1 hour'\n    AGGREGATION_WINDOW='1 day';\n\nCREATE OR REPLACE MODEL MONITOR MORTGAGE_LENDING_OPTIMIZED_MODEL_MONITOR\nWITH\n    MODEL=MORTGAGE_LENDING_MLOPS_0\n    VERSION=XGB_OPTIMIZED\n    FUNCTION=predict\n    SOURCE=DEMO_MORTGAGE_LENDING_TEST_0\n    BASELINE=DEMO_MORTGAGE_LENDING_TRAIN_0\n    TIMESTAMP_COLUMN=TIMESTAMP\n    PREDICTION_CLASS_COLUMNS=(XGB_OPTIMIZED_PREDICTION)  \n    ACTUAL_CLASS_COLUMNS=(MORTGAGERESPONSE)\n    ID_COLUMNS=(LOAN_ID)\n    WAREHOUSE=E2E_SNOW_MLOPS_WH\n    REFRESH_INTERVAL='12 hours'\n    AGGREGATION_WINDOW='1 day';\n```\n\n### Query Model Drift Metrics\n\n```sql\nSELECT * FROM TABLE(MODEL_MONITOR_DRIFT_METRIC(\n'MORTGAGE_LENDING_BASE_MODEL_MONITOR', -- model monitor to use\n'DIFFERENCE_OF_MEANS', -- metric for computing drift\n'XGB_BASE_PREDICTION', -- column to compute drift on\n'1 DAY',  -- day granularity for drift computation\nDATEADD(DAY, -90, CURRENT_DATE()), -- end date\nDATEADD(DAY, -60, CURRENT_DATE()) -- start date\n))\n```\n\n## Conclusion And Resources\n\nYou just walked through a guided experience building and deploying a complete end-to-end machine learning workflow within [Snowflake ML](http://www.snowflake.com/ml) for a mortgage lending prediction case. The workflow covers feature engineering with Snowflake Feature Store, model training and hyperparameter optimization using Snowflake ML APIs, model logging and management with Snowflake Model Registry, and model performance tracking and drift detection via ML Observability. \n\nReady for more? After you complete this quickstart, you can try another guided ML example [here](https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart). \n\nRelated Resources\n- [Snowflake ML Docs](https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview)\n- [Snowflake ML Resources](http://www.snowflake.com/ml)\n- [Best Practices for Production ML](https://www.snowflake.com/en/engineering-blog/best-practices-for-production-ml/)\n- [Agentic Machine Learning Best Practices with Snowflake CoCo](https://www.snowflake.com/en/engineering-blog/agentic-ml-best-practices-cortex-code/)\n- [Fork Notebook on GitHub](https://github.com/Snowflake-Labs/sfguide-build-end-to-end-ml-workflow-in-snowflake/blob/main/train_deploy_monitor_ML_in_snowflake.ipynb?_fsi=hnlih63N&_fsi=hnlih63N)\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2025/quickstart-reference-architecture.pdf)\n","title":"Quickstart Article Body","dataType":"string","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-20f2e7b538","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-cd50218f54","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-e06e555d27","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-64d34dc82b","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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