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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-d6c42ce56d","quickstartHeroTitle":{"lines":["Getting Started with End-to-End Customer Targeting Using Snowflake ML"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Ranjeeta Pegu","quickstartHeroForkRepoLink":{"id":"button-105c1c8449","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/getting-started-with-e2e-customer-targeting-with-snowflake-ml"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","text":"Fork Repo","linkType":"SNOWFLAKE_EXTERNAL"},"quickstartHeroBreadcrumbs":[{"title":"Getting Started with End-to-End Customer Targeting Using Snowflake ML","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/getting-started-with-e2e-customer-targeting-with-snowflake-ml","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for 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how to leverage Snowflake ML to build and deploy propensity-based targeting models, enabling businesses to predict customer behaviors, segment audiences, and deliver personalized marketing campaigns at scale for maximum impact","title":"Getting Started with End-to-End Customer Targeting Using Snowflake ML","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ECompanies use machine learning for targeted customer campaigns to improve engagement and conversions. The intent of this notebook is to demonstrate how you can implement propensity-based targeting directly in Snowflake, where your data resides. This approach eliminates the need for data movement and ensures faster, more efficient turnarounds.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, we will walk through the end-to-end machine learning workflow within Snowflake, covering key stages such as feature engineering, managing a feature store, model training, and model registry. We will also cover inferencing using the trained models and demonstrate how to integrate these steps within a Snowflake Notebook. By leveraging Snowpark for scalable data processing, the Feature Store for centralized feature management, and the Model Registry for model deployment and version control, all data and processes remain within Snowflake, ensuring faster and more efficient workflows.\u003C/p\u003E\n","\u003Cp\u003ELearn about Snowflake ML \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003Ehere\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can get more information \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/README.md\"\u003Ehere\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to \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 role\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to Generate Synthetic Data\u003C/strong\u003E:\nCreate realistic, structured datasets using Snowpark with numeric, categorical, and time-based features.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to Build a Feature Store\u003C/strong\u003E:\nDefine entities, register feature views, and manage versioned features in Snowflake.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to do Feature Reduction\u003C/strong\u003E: Slice datasets and apply correlation/variance thresholding for dimensionality reduction.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to do Model Training and Tuning\u003C/strong\u003E: Use Snowflake ML to train XGBoost, Random Forest, etc., and optimize with Grid Search.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to Register and Deploy Models\u003C/strong\u003E: Log models with versioning and run scalable in-database predictions from feature views.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBuild a Feature Store: Create and manage feature entries for model training within Snowflake.\u003C/li\u003E\u003Cli\u003ETrain Classification Models: Use techniques like XGBoost, Random Forest, and Grid Search for model training and hyperparameter tuning.\u003C/li\u003E\u003Cli\u003EDeploy & Predict: Log, register models in Snowflake ML, and run predictions directly using feature views.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1.\u003C/strong\u003E To set up the environment, download the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/Setup.sql\"\u003Esqlsetup.sql\u003C/a\u003E script from GitHub and execute all the statements in a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs?_fsi=THrZMtDg,%20THrZMtDg&_fsi=THrZMtDg,%20THrZMtDg#create-worksheets-from-a-sql-file\"\u003ESnowflake worksheet\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe script includes the following SQL operations\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE ACCOUNTADMIN;\n--create database\ncreate database if  not exists ML_MODELS;\n--create schema\ncreate schema   if  not exists  ML_MODELS.DS;\ncreate schema if not exists ML_MODELS.FEATURE_STORE;\ncreate schema if not exists ML_MODELS.ML_REGISTRY;\n\n--warehouse \ncreate warehouse if not exists  DS_W WAREHOUSE_SIZE = MEDIUM;\n--snowpark optimized warehouse\nCREATE OR REPLACE WAREHOUSE SNOWPARK_OPT_WH  WITH\n  WAREHOUSE_SIZE = 'MEDIUM'\n  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED';\n\n-- snowflake internal stage\ncreate stage if not exists ML_MODELS.DS.MODEL_OBJECT   ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n\n--create role \nCREATE ROLE if not exists FR_SCIENTIST;\n--access to role \ngrant usage on database ML_MODELS to role FR_SCIENTIST;\ngrant role FR_SCIENTIST to current_users();\ngrant all on schema ML_MODELS.DS to role FR_SCIENTIST;\ngrant all on schema ML_MODELS.FEATURE_STORE to role FR_SCIENTIST;\ngrant all on schema ML_MODELS.ML_REGISTRY to role FR_SCIENTIST;\ngrant usage on warehouse ML_FS_WH to role FR_SCIENTIST;\ngrant read on stage  ML_MODELS.DS.MODEL_STAGE  to role FR_SCIENTIST;\ngrant write on stage  ML_MODELS.DS.MODEL_STAGE  to role FR_SCIENTIST;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2.\u003C/strong\u003E Download the all the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/tree/main\"\u003Eipynb files\u003C/a\u003E from the git repository to your local machine\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate Realistic Synthetic Data\u003C/h2\u003E\n","\u003Cp\u003EWe will generate a synthetic dataset consisting of 100,000 rows and 508 columns, including member_id, a binary target variable, and a mix of numerical and categorical features. Using Scikit-Learn's make_classification, we will create 150 base features, then augment the dataset with 200 low-variance features, 150 highly correlated features, 5 categorical columns, and introduce missing values in selected fields.\u003C/p\u003E\n","\u003Cp\u003ETo reproduce this in your environment, import the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/DATA_CREATOR.ipynb\"\u003EDATA_CREATOR.ipynb\u003C/a\u003E, run all cells, and ensure you are using the FR_SCIENTIST role in Snowflake.\u003C/p\u003E\n","\u003Cp\u003ESnippet to generate the Pandas Dataframe\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eimport numpy as np\nimport pandas as pd\nfrom sklearn.datasets import make_classification\n\n# Parameters\nn_samples = 100000\nbase_features = 150\nlow_variance_features = 200\ncorrelated_features = 150\ntotal_numeric_features = base_features + low_variance_features + correlated_features\n\n# Step 1: Generate base numerical features and target\nX, y = make_classification(\n    n_samples=n_samples,\n    n_features=base_features,\n    n_informative=60,\n    n_redundant=60,\n    n_repeated=0,\n    n_classes=2,\n    random_state=42,\n    shuffle=False\n)\n\n# Create DataFrame for base features\ndf = pd.DataFrame(X, columns=[f'FEATURE_{i}' for i in range(base_features)])\ndf['TARGET'] = y\n\n# Step 2: Add 200 Low-Variance Features\nfor i in range(1, low_variance_features + 1):\n    if i == 1:\n        df[f'FEATURE_LOW_VAR_{i}'] = 1  # Constant column\n    else:\n        df[f'FEATURE_LOW_VAR_{i}'] = np.random.choice([0, 1], size=n_samples, p=[0.98, 0.02])\n\n# Step 3: Add 150 Highly Correlated Features\nfor i in range(1, correlated_features + 1):\n    source_feature = f'FEATURE_{(i - 1) % base_features}'  # Cycle through base features\n    df[f'FEATURE_CORR_{i}'] = df[source_feature] * 0.95 + np.random.normal(0, 0.01, n_samples)\n\n# Step 4: Add 5 Specific Categorical Columns with realistic values\ndf['CAT_1'] = np.random.choice(['Male', 'Female'], size=n_samples)\ndf['CAT_2'] = np.random.choice(['online', 'retail'], size=n_samples)\ndf['CAT_3'] = np.random.choice(['tier_1', 'tier_2', 'tier_3'], size=n_samples)\ndf['CAT_4'] = np.random.choice(['credit', 'debit'], size=n_samples)\ndf['CAT_5'] = np.random.choice(['single', 'family'], size=n_samples)\n\n# Step 5: Add Missing Values\ndef add_missing_values(df, cols, fraction=0.05):\n    for col in cols:\n        missing_indices = df.sample(frac=fraction, random_state=42).index\n        df.loc[missing_indices, col] = np.nan\n    return df\n\n# Introduce missing values in numeric and categorical columns\nnumeric_missing = ['FEATURE_0', 'FEATURE_10', 'FEATURE_50', 'FEATURE_LOW_VAR_2', 'FEATURE_CORR_1']\ncategorical_missing = ['CAT_1', 'CAT_3', 'CAT_5']\n\ndf = add_missing_values(df, numeric_missing)\ndf = add_missing_values(df, categorical_missing)\n\n# Step 6: Add MEMBER_ID Column\ndf['MEMBER_ID'] = [f'member_{i}' for i in range(len(df))]\n\n# Step 7: Add REF_MMYY Column with random assignment of '042025' or '052025'\ndf['REF_MMYY'] = np.random.choice(['042025', '052025'], size=n_samples)\n\n# Final shape check\nprint(f"Final Data Shape: {df.shape}")  # Should be (100000, ~507)\n\n# Optional: Preview the data\n# print(df.head())\n\n)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we split the full dataset into five DataFrames, evenly distributing the 508 features across them. Each DataFrame includes key columns like member_id and ref_mmyy, and is saved as a Snowflake table to serve as an input source for building the Feature Store.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFeature Store Creation in Snowflake\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/overview\"\u003ESnowflake Feature Store\u003C/a\u003E enables data scientists and ML engineers to create, manage, and reuse machine learning features entirely within Snowflake. It simplifies the end-to-end workflow by keeping features close to the data. In this example, we'll demonstrate how to define entities and create feature views directly from existing Snowflake tables, making your features accessible for both training and inference tasks.\u003C/p\u003E\n","\u003Cp\u003ETo complete this step, download the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/01_FeatureStore_Creation.ipynb\"\u003E01_FeatureStore_Creation.ipynb\u003C/a\u003E and import it into your Snowflake environment. Once imported, run all cells to execute the setup.\u003C/p\u003E\n","\u003Cp\u003ELet's now walk through the key steps covered in the notebook.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECreate or Connect to Feature Store\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis code registers a new feature store or connects to an existing one in your Snowflake environment.\u003Cbr\u003E\n\u003Cstrong\u003ENote\u003C/strong\u003E: Ensure the feature store schema (\u003Ccode\u003Efs_schema\u003C/code\u003E) is created beforehand to organize all objects under the correct schema.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E# Create/Reference Snowflake Feature Store for Training (Development) Environment\ntry: \n    fs = FeatureStore(\n        session=session,        \n        database=working_database, \n        name=fs_schema,\n        default_warehouse=warehouse\n    )\nexcept:\n    # need privs to create fs if not exists\n    fs = FeatureStore(\n        session=session,        \n        database=working_database, \n        name=fs_schema, \n        default_warehouse=warehouse,\n        creation_mode=CreationMode.CREATE_IF_NOT_EXIST\n    )\n## define the primary or join keys \njoin_keys = ["MEMBER_ID", "REF_MMYY"]\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ERegister the Feature Views\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis snippet shows how to define entities, which are used to register feature views in the Feature Store.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Edef register_feature(fs, entity_nm, fv_version, feature_df, join_keys):\n    """\n    Registers an entity and a feature view in the feature store if they do not already exist.\n\n    Parameters:\n        fs (FeatureStore): Feature store client instance\n        entity_nm (str): Name of the entity\n        fv_version (str): Version of the feature view\n        feature_df (DataFrame):  DataFrame containing feature data\n        join_keys (list): List of join keys for the entity\n\n    Returns:\n        FeatureView: The registered or retrieved FeatureView instance\n    """\n\n    fv_name = f"FV_FEATURE_{entity_nm}"\n\n    # Check if entity exists\n    entity_names_json = fs.list_entities().select(F.to_json(F.array_agg("NAME", True))).collect()[0][0]\n    existing_entities = json.loads(entity_names_json)\n\n    if entity_nm not in existing_entities:\n        entity_instance = Entity(name=entity_nm, join_keys=join_keys, desc=f"Primary Keys for {entity_nm}")\n        fs.register_entity(entity_instance)\n    else:\n        entity_instance = fs.get_entity(entity_nm)\n\n    # Try to get the FeatureView; register it if it doesn't exist\n    try:\n        fv_feature_instance = fs.get_feature_view(fv_name, fv_version)\n    except:\n        fv_feature_instance = FeatureView(\n            name=fv_name,\n            entities=[entity_instance],\n            feature_df=feature_df\n        )\n        fs.register_feature_view(fv_feature_instance, version=fv_version, block=True)\n\n    return fv_feature_instance\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Dataset Creation\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETo proceed with feature engineering and dimensionality reduction, we first prepare our dataset.\u003Cbr\u003E\n\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/dataset\"\u003EDatasets\u003C/a\u003E are new Snowflake schema-level objects specifically designed for machine learning workflows.\u003Cbr\u003E\nSnowflake Datasets hold collections of data organized into versions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E#retrieve the entity views\n\nfv_feature_ent1_instance  = fs.get_feature_view("FV_FEATURE_ENT_1", "V_1")\nfv_feature_ent2_instance  = fs.get_feature_view("FV_FEATURE_ENT_2", "V_1")\nfv_feature_ent3_instance  = fs.get_feature_view("FV_FEATURE_ENT_3", "V_1")\nfv_feature_ent4_instance  = fs.get_feature_view("FV_FEATURE_ENT_4", "V_1")\n\n\nfv_list = [fv_feature_ent1_instance, \n           fv_feature_ent2_instance, \n           fv_feature_ent3_instance,\n           fv_feature_ent4_instance] \n\nds_cols = []\nslice_list = []\nfor fv in fv_list:\n    fv_cols = list(fv._feature_desc)\n    slice_cols = [col for col in fv_cols if col not in ds_cols]\n    #fv = fv.slice(slice_cols)\n    slice_list.append(fv.slice(slice_cols))\n    ds_cols += fv_cols\n\n ## create DS   \ndataset = fs.generate_dataset(\n    name=f"{working_database}.{working_schema}.POC_DATASET",\n    spine_df=universe_sdf,\n    features = slice_list,\n    version="V_1",\n    output_type="table",\n    spine_label_cols=["TARGET"],\n    desc="training dataset for ml poc"\n) \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECheck the Dataset Table\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou can check the contents of the dataset table \u003Cstrong\u003EPOC_DATASET\u003C/strong\u003E using the query below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eselect * from POC_DATASET;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFeature Reduction\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we preprocess the dataset by performing feature reduction, removing redundant or irrelevant features before model training. For feature reduction, we will employ techniques such as Variance Threshold and Correlation Analysis. There are numerous other dimensionality reduction techniques available, which you can explore further \u003Ca href=\"https://en.wikipedia.org/wiki/Dimensionality_reduction\"\u003Ehere\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003EWhy to do Feature Reduction ?\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EFeature reduction simplifies models, reduces overfitting, and improves computational efficiency.\u003C/p\u003E\n","\u003Cp\u003ETo complete this step, import the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/02_Feature_Reduction.ipynb\"\u003E02_Feature_Reduction.ipynb\u003C/a\u003E into your Snowflake environment. Once imported, run all cells to execute the setup.\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003EVariance Threshold:\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ERemoves features with low variance that offer little predictive value, such as those with near-constant values across samples. Here's a code snippet from the notebook\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efrom snowflake.snowpark import DataFrame\n#from snowflake.snowpark.functions import var_pop\n\n## get all columns with stringType= type\nexcluded = ['MEMBER_ID', 'TARGET','REF_MMYYYY','CAT_1','CAT_2','CAT_3','CAT_4','CAT_5']\nnum_cols = [col for col in sdf.columns if col not in excluded]\n\nsession.use_warehouse('SNOWPARK_OPT_W')\nprint(f'number of features before the variance threshold {len(num_cols)}')\n\n# get the\nvariance_df = sdf.select([F.var_pop(F.col(c)).alias(c) for c in num_cols])\n\nvariance_df = variance_df.to_pandas()\ncols_below_threshold  = variance_df.columns[(variance_df  < 0.1).all()]\nprint( f" total cols having variance threshold less than 0.1  is {len(cols_below_threshold)}")\n\nsdf=sdf.drop(*cols_below_threshold )\n\nprint(f'number of features after applying the variance threshold  {len(cols_below_threshold)}')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ECorrelation Analysis:\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EIdentifies and removes highly correlated features (e.g., correlation > 0.8), which provide redundant information and may lead to instability. Here's a code snippet from the notebook\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efrom snowflake.ml.modeling.metrics.correlation import correlation\n\n\ndef snf_correlation_thresholder(df, features, corr_threshold: float):\n    assert 0 < corr_threshold <= 1, "Correlation threshold must be in range (0, 1]."\n    \n    corr_features = set()\n    corr_matrix = correlation(df=sdf)\n\n    # Compute pairwise correlations directly in Snowpark\n    for i in range(len(features)):\n        for j in range(i + 1, len(features)):\n            if (abs(corr_matrix.iloc[i][j])) >=  corr_threshold:\n            #col1, col2 = features[i], features[j]\n            #corr_value = df.select(corr(col(col1), col(col2)).alias('corr')).collect()[0]['CORR']\n            \n           # if corr_value is not None and abs(corr_value) >= corr_threshold:\n                # Mark the second feature for removal to avoid keeping highly correlated pairs\n                #corr_features.add(col2)\n                corr_features.add(features[j])\n    \n    # Drop correlated features if any\n    if corr_features:\n        df = df.drop(*corr_features)\n        \n    return df\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, we reduce the total number of columns in the dataset from 508 to 150. This refined dataset now serves as the input for the next step: model training.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Training\u003C/h2\u003E\n","\u003Cp\u003EIn this step, we will train the model using Snowflake ML, beginning by creating a preprocessing pipeline that will convert categorical variables into numerical format through one-hot encoding and apply Min-Max scaling to standardize numerical features.\u003C/p\u003E\n","\u003Cp\u003EAfter preprocessing, we will experiment with several modeling techniques, including XGBoost and Random Forest. Model tuning will be conducted using GridSearch, and all training will be executed within Snowflake ML.\u003C/p\u003E\n","\u003Cp\u003ETo complete this step, import the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/03_Model_Training.ipynb\"\u003E03_Model_Training.ipynb\u003C/a\u003E into your Snowflake environment. Once imported, run all cells to execute the setup.\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003EPreprocessor Pipeline\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EHere’s the code snippet for the preprocessing pipeline.\u003Cbr\u003E\nThe pipeline saves the model as a \u003Ccode\u003E.joblib\u003C/code\u003E file in a Snowflake stage for reusability.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efrom snowflake.ml.modeling.pipeline import Pipeline \nfrom snowflake.ml.modeling.preprocessing import MinMaxScaler , OneHotEncoder\n\npreprocessing_pipeline = Pipeline(\n    steps=[\n        \n        ("OHE",\n         OneHotEncoder(input_cols=cat_cols,\n                       output_cols=cat_cols,\n                       drop_input_cols=True,\n                       drop="first",\n                       handle_unknown="ignore",)\n         ),\n        ("MMS",MinMaxScaler(clip=True, \n                            input_cols=num_cols,\n                            output_cols=num_cols,))\n    ]\n\n)\n\njoblib.dump(preprocessing_pipeline, 'preprocessing_pipeline.joblib')\n#upload\nsession.file.put('preprocessing_pipeline.joblib',\n                 stage,auto_compress=False)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003EXGBoost Classifier in Snowflake ML\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ESnippet for training an XGBoost Classifier using Snowflake ML:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efrom snowflake.ml.modeling.xgboost import XGBClassifier\nXGB_Classifier= XGBClassifier(\n    input_cols=FEATURE_COLS ,\n    label_cols=label_col,\n    output_cols=OUTPUT_COLUMNS\n)\n# Train\nXGB_Classifier.fit(train_df)\n\n#  evaluation \npredict_on_training_data = XGB_Classifier.predict(train_df)\n\ntraining_accuracy = accuracy_score(df=predict_on_training_data, \n                                   y_true_col_names=["TARGET"],\n                                   y_pred_col_names=["PREDICTED_TARGET"])\n\n\nresult = XGB_Classifier.predict(test_df)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ERandom Forest Classifier in Snowflake ML\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ESnippet for training a Random Forest Classifier using Snowflake ML:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efrom snowflake.ml.modeling.ensemble import RandomForestClassifier\n\nFEATURE_COLS = get_features(train_df, label_col)\nOUTPUT_COLUMNS="PREDICTED_TARGET"\nlabel_col='TARGET'\n\n\nRandomForest= RandomForestClassifier(\n    input_cols=FEATURE_COLS ,\n    label_cols=label_col,\n    output_cols=OUTPUT_COLUMNS\n)\n# Train\nRandomForest.fit(train_df)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003EModel Evaluation\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ERun predictions on the training dataset using the trained Random Forest model:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Epredict_on_training_data = RandomForest.predict(train_df)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003EGrid Search in Snowflake ML\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ESnippet for performing Grid Search hyperparameter tuning using Snowflake ML:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E## parameter grid \nFEATURE_COLS = get_features(train_df, label_col)\nOUTPUT_COLUMNS="PREDICTED_TARGET"\nlabel_col='TARGET'\n\n\n\nparameters = {\n        "n_estimators": [100, 300, 500],\n        "learning_rate": [0.1, 0.3, 0.5],\n        "max_depth": list(range(3, 5, 1)),\n        "min_child_weight": list(range(3, 5, 1)),\n    }\n    \nn_folds = 5\n\nestimator = XGBClassifier()\n\nGridSearch_clf = GridSearchCV(estimator= estimator,\n                   param_grid=parameters ,\n                   cv = n_folds,\n                   input_cols=FEATURE_COLS ,\n                   label_cols=label_col,\n                   output_cols=OUTPUT_COLUMNS\n                   )\nGridSearch_clf.fit(train_df)\n\nresult = GridSearch_clf.predict(test_df )\nprint(GridSearch_clf.to_sklearn().best_estimator_)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E** Model Metrics **\nSnippet showing how to get the model metrics natively using Snowflake ML\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E# snowpark ML metrics\nfrom snowflake.ml.modeling.metrics import accuracy_score,f1_score,precision_score,roc_auc_score,roc_curve,recall_score\n\nmetrics = {\n"accuracy":accuracy_score(df=result ,\n                          y_true_col_names="TARGET", \n                          y_pred_col_names="PREDICTED_TARGET"),\n\n"precision":precision_score(df=result,\n                            y_true_col_names="TARGET", \n                            y_pred_col_names="PREDICTED_TARGET"),\n\n\n"recall": recall_score(df=result, \n                       y_true_col_names="TARGET",\n                       y_pred_col_names="PREDICTED_TARGET"),\n\n\n\n"f1_score":f1_score(df=result,\n                   y_true_col_names="TARGET",\n                   y_pred_col_names="PREDICTED_TARGET"),\n"confusion_matrix":confusion_matrix(df=result, \n                                    y_true_col_name="TARGET",\n                                    y_pred_col_name="PREDICTED_TARGET").tolist()\n}\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELoging Model into Model registry\u003C/strong\u003E\nSnippet to register a model in Snowflake ML\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E# Get sample input data to pass into the registry logging function\nX = train_df.select(FEATURE_COLS).limit(100)\ndb = working_database \nschema =model_registry_schema \n\n# Create a registry and log the model\nreg = Registry(session=session, database_name=db, schema_name=schema)\n\n\nmodel_name = "ML_XGBOOST_MODEL"\nversion_name = "v1"\n\n# Let's first log the very first model we trained\nmv = reg.log_model(\n    model_name=model_name,\n    version_name=version_name,\n    model= XGB_Classifier,\n    metrics=metrics ,\n    sample_input_data=X, # to provide the feature schema\n)\n\n\n# Add a description\nmv.comment = """This is the first iteration of our ml poc  model. \nIt is used for demo purposes and it is simple xgboost model."""\n\n\n# Let's confirm they were added\nreg.get_model(model_name).show_versions()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Inferencing and Scheduling\u003C/h2\u003E\n","\u003Cp\u003EIn this section, you'll learn how to perform batch inferencing using Snowflake ML by:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELeveraging features stored in the Feature Store\u003C/li\u003E\u003Cli\u003ELoading the model signature from the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003ERunning predictions at scale within Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis approach enables efficient generation of model predictions for large datasets, all within the Snowflake platform. Additionally, the notebook can be \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-schedule\"\u003Escheduled\u003C/a\u003E to run at regular intervals, facilitating fully automated and production-grade batch scoring workflows.\u003C/p\u003E\n","\u003Cp\u003ETo proceed, please import the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/04_Batch_Inferencing.ipynb\"\u003E04_Batch_Inferencing.ipynb\u003C/a\u003E and run all the cells. Let’s now dive into the code details.\u003C/p\u003E\n","\u003Ch3\u003E1. Retrieve Features from the Feature Store\u003C/h3\u003E\n","\u003Cp\u003ERetrieve features from the Feature Store using the model signature of the selected model from the Model Registry.\u003C/p\u003E\n","\u003Cp\u003EBelow is the code snippet from the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efv_feature_ent1_instance  = fs.get_feature_view("FV_FEATURE_ENT_1", "V_1")\nfv_feature_ent2_instance  = fs.get_feature_view("FV_FEATURE_ENT_2", "V_1")\nfv_feature_ent3_instance  = fs.get_feature_view("FV_FEATURE_ENT_3", "V_1")\nfv_feature_ent4_instance  = fs.get_feature_view("FV_FEATURE_ENT_4", "V_1")\n\n\nfv_list = [fv_feature_ent1_instance, \n           fv_feature_ent2_instance, \n           fv_feature_ent3_instance,\n           fv_feature_ent4_instance] \n\n\nuniverse_tbl = '.'.join([input_database, input_schema, 'DEMO_TARGETS_TBL'])\nuniverse_sdf            = session.table(universe_tbl).filter(F.col("REF_MMYY") == ref_mmyyyy)\n\n\n#get the input signature from the desired model from the model registr\n\nreg = Registry(session, database_name = working_database,schema_name = model_registry_schema)\nreg.show_models()\nmv = reg.get_model(model_name).version("v1")\n# the input signature of model\ninput_signature = mv.show_functions()[0].get("signature").inputs\ninput_cols = [c.name for c in input_signature]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E2 Create Dataset for Inference\u003C/h3\u003E\n","\u003Cp\u003ESnippet of code to generate the dataset used for inference:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Edataset = fs.generate_dataset(\n    name=f"{working_database}.{working_schema}.INFERENCE_DATASET",\n    spine_df=universe_sdf,\n    features = slice_list,\n    version=dataset_version,\n    #output_type="table",\n    spine_label_cols=["TARGET"],\n    desc="training dataset for ml demo"\n)    \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E3. Run Preprocessing\u003C/h3\u003E\n","\u003Cp\u003ESnippet of code to run preprocessing on unseen or inference data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Esession.file.get(f'{stage}/preprocessing_pipeline.joblib', '/tmp')\nPIPELINE_FILE = '/tmp/preprocessing_pipeline.joblib'\n\npreprocessing_pipeline = joblib.load(PIPELINE_FILE)\n\ndf=preprocessing_pipeline.fit(df).transform(df)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E3. Run Prediction\u003C/h3\u003E\n","\u003Cp\u003ESnippet of code to run prediction on an unseen or prediction dataset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eprediction_result = mv.run(df, function_name ="PREDICT")\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You’ve successfully built an end-to-end customer targeting model in a Snowflake Notebook and logged the trained model to the Snowflake ML Registry, making it ready for inference and future use.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to generate realistic synthetic data in Snowpark and save it as Snowflake tables.\u003C/li\u003E\u003Cli\u003EHow to define entities and register feature views in the Feature Store.\u003C/li\u003E\u003Cli\u003EHow to perform feature engineering and feature reduction.\u003C/li\u003E\u003Cli\u003EHow to train and tune models within Snowflake ML.\u003C/li\u003E\u003Cli\u003EHow to log and register models in the Snowflake ML registry.\u003C/li\u003E\u003Cli\u003EHow to run predictions on new data using the registered model within Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/tree/main\"\u003EGitHub Repo\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/bring-your-own-model-types\"\u003ESnowflake Logging Custom Models\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/data-cloud/snowflake-ml/\"\u003ESnowflake ML\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://en.wikipedia.org/wiki/Category:Classification_algorithms\"\u003Eclassification Model\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nCompanies use machine learning for targeted customer campaigns to improve engagement and conversions. The intent of this notebook is to demonstrate how you can implement propensity-based targeting directly in Snowflake, where your data resides. This approach eliminates the need for data movement and ensures faster, more efficient turnarounds.\n\nIn this guide, we will walk through the end-to-end machine learning workflow within Snowflake, covering key stages such as feature engineering, managing a feature store, model training, and model registry. We will also cover inferencing using the trained models and demonstrate how to integrate these steps within a Snowflake Notebook. By leveraging Snowpark for scalable data processing, the Feature Store for centralized feature management, and the Model Registry for model deployment and version control, all data and processes remain within Snowflake, ensuring faster and more efficient workflows.\n\nLearn about Snowflake ML [here](https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview)\n\nYou can get more information [here](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/README.md)\n\n\n\n### Prerequisites\n-  Access to [snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with ACCOUNTADMIN role\n\n\n### What You’ll Learn \n-  **How to Generate Synthetic Data**:\n  Create realistic, structured datasets using Snowpark with numeric, categorical, and time-based features.\n\n-  **How to Build a Feature Store**:\nDefine entities, register feature views, and manage versioned features in Snowflake.\n\n-  **How to do Feature Reduction**: Slice datasets and apply correlation/variance thresholding for dimensionality reduction.\n\n-  **How to do Model Training and Tuning**: Use Snowflake ML to train XGBoost, Random Forest, etc., and optimize with Grid Search.\n\n-  **How to Register and Deploy Models**: Log models with versioning and run scalable in-database predictions from feature views.\n\n\n\n\n### What You’ll Build \n\n- Build a Feature Store: Create and manage feature entries for model training within Snowflake.\n- Train Classification Models: Use techniques like XGBoost, Random Forest, and Grid Search for model training and hyperparameter tuning.\n- Deploy & Predict: Log, register models in Snowflake ML, and run predictions directly using feature views.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n**Step 1.** To set up the environment, download the [sqlsetup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/Setup.sql) script from GitHub and execute all the statements in a [Snowflake worksheet](https://docs.snowflake.com/en/user-guide/ui-snowsight-worksheets-gs?_fsi=THrZMtDg,%20THrZMtDg&_fsi=THrZMtDg,%20THrZMtDg#create-worksheets-from-a-sql-file).\n\nThe script includes the following SQL operations\n```\nUSE ROLE ACCOUNTADMIN;\n--create database\ncreate database if  not exists ML_MODELS;\n--create schema\ncreate schema   if  not exists  ML_MODELS.DS;\ncreate schema if not exists ML_MODELS.FEATURE_STORE;\ncreate schema if not exists ML_MODELS.ML_REGISTRY;\n\n--warehouse \ncreate warehouse if not exists  DS_W WAREHOUSE_SIZE = MEDIUM;\n--snowpark optimized warehouse\nCREATE OR REPLACE WAREHOUSE SNOWPARK_OPT_WH  WITH\n  WAREHOUSE_SIZE = 'MEDIUM'\n  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED';\n\n-- snowflake internal stage\ncreate stage if not exists ML_MODELS.DS.MODEL_OBJECT   ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n\n--create role \nCREATE ROLE if not exists FR_SCIENTIST;\n--access to role \ngrant usage on database ML_MODELS to role FR_SCIENTIST;\ngrant role FR_SCIENTIST to current_users();\ngrant all on schema ML_MODELS.DS to role FR_SCIENTIST;\ngrant all on schema ML_MODELS.FEATURE_STORE to role FR_SCIENTIST;\ngrant all on schema ML_MODELS.ML_REGISTRY to role FR_SCIENTIST;\ngrant usage on warehouse ML_FS_WH to role FR_SCIENTIST;\ngrant read on stage  ML_MODELS.DS.MODEL_STAGE  to role FR_SCIENTIST;\ngrant write on stage  ML_MODELS.DS.MODEL_STAGE  to role FR_SCIENTIST;\n\n```\n\n**Step 2.** Download the all the [ipynb files](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/tree/main) from the git repository to your local machine\n \n\n\n\u003C!-- ------------------------ --\u003E\n## Generate Realistic Synthetic Data \n\nWe will generate a synthetic dataset consisting of 100,000 rows and 508 columns, including member_id, a binary target variable, and a mix of numerical and categorical features. Using Scikit-Learn's make_classification, we will create 150 base features, then augment the dataset with 200 low-variance features, 150 highly correlated features, 5 categorical columns, and introduce missing values in selected fields.\n\nTo reproduce this in your environment, import the notebook [DATA_CREATOR.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/DATA_CREATOR.ipynb), run all cells, and ensure you are using the FR_SCIENTIST role in Snowflake.\n\nSnippet to generate the Pandas Dataframe\n\n```\nimport numpy as np\nimport pandas as pd\nfrom sklearn.datasets import make_classification\n\n# Parameters\nn_samples = 100000\nbase_features = 150\nlow_variance_features = 200\ncorrelated_features = 150\ntotal_numeric_features = base_features + low_variance_features + correlated_features\n\n# Step 1: Generate base numerical features and target\nX, y = make_classification(\n    n_samples=n_samples,\n    n_features=base_features,\n    n_informative=60,\n    n_redundant=60,\n    n_repeated=0,\n    n_classes=2,\n    random_state=42,\n    shuffle=False\n)\n\n# Create DataFrame for base features\ndf = pd.DataFrame(X, columns=[f'FEATURE_{i}' for i in range(base_features)])\ndf['TARGET'] = y\n\n# Step 2: Add 200 Low-Variance Features\nfor i in range(1, low_variance_features + 1):\n    if i == 1:\n        df[f'FEATURE_LOW_VAR_{i}'] = 1  # Constant column\n    else:\n        df[f'FEATURE_LOW_VAR_{i}'] = np.random.choice([0, 1], size=n_samples, p=[0.98, 0.02])\n\n# Step 3: Add 150 Highly Correlated Features\nfor i in range(1, correlated_features + 1):\n    source_feature = f'FEATURE_{(i - 1) % base_features}'  # Cycle through base features\n    df[f'FEATURE_CORR_{i}'] = df[source_feature] * 0.95 + np.random.normal(0, 0.01, n_samples)\n\n# Step 4: Add 5 Specific Categorical Columns with realistic values\ndf['CAT_1'] = np.random.choice(['Male', 'Female'], size=n_samples)\ndf['CAT_2'] = np.random.choice(['online', 'retail'], size=n_samples)\ndf['CAT_3'] = np.random.choice(['tier_1', 'tier_2', 'tier_3'], size=n_samples)\ndf['CAT_4'] = np.random.choice(['credit', 'debit'], size=n_samples)\ndf['CAT_5'] = np.random.choice(['single', 'family'], size=n_samples)\n\n# Step 5: Add Missing Values\ndef add_missing_values(df, cols, fraction=0.05):\n    for col in cols:\n        missing_indices = df.sample(frac=fraction, random_state=42).index\n        df.loc[missing_indices, col] = np.nan\n    return df\n\n# Introduce missing values in numeric and categorical columns\nnumeric_missing = ['FEATURE_0', 'FEATURE_10', 'FEATURE_50', 'FEATURE_LOW_VAR_2', 'FEATURE_CORR_1']\ncategorical_missing = ['CAT_1', 'CAT_3', 'CAT_5']\n\ndf = add_missing_values(df, numeric_missing)\ndf = add_missing_values(df, categorical_missing)\n\n# Step 6: Add MEMBER_ID Column\ndf['MEMBER_ID'] = [f'member_{i}' for i in range(len(df))]\n\n# Step 7: Add REF_MMYY Column with random assignment of '042025' or '052025'\ndf['REF_MMYY'] = np.random.choice(['042025', '052025'], size=n_samples)\n\n# Final shape check\nprint(f\"Final Data Shape: {df.shape}\")  # Should be (100000, ~507)\n\n# Optional: Preview the data\n# print(df.head())\n\n)\n\n```\nNext, we split the full dataset into five DataFrames, evenly distributing the 508 features across them. Each DataFrame includes key columns like member_id and ref_mmyy, and is saved as a Snowflake table to serve as an input source for building the Feature Store.\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Feature Store Creation in Snowflake\n\nThe [Snowflake Feature Store](https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/overview) enables data scientists and ML engineers to create, manage, and reuse machine learning features entirely within Snowflake. It simplifies the end-to-end workflow by keeping features close to the data. In this example, we'll demonstrate how to define entities and create feature views directly from existing Snowflake tables, making your features accessible for both training and inference tasks.\n\nTo complete this step, download the notebook [01_FeatureStore_Creation.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/01_FeatureStore_Creation.ipynb) and import it into your Snowflake environment. Once imported, run all cells to execute the setup. \n\nLet's now walk through the key steps covered in the notebook.\n\n- **Create or Connect to Feature Store**\n\nThis code registers a new feature store or connects to an existing one in your Snowflake environment.  \n**Note**: Ensure the feature store schema (`fs_schema`) is created beforehand to organize all objects under the correct schema.\n\n\n```\n# Create/Reference Snowflake Feature Store for Training (Development) Environment\ntry: \n    fs = FeatureStore(\n        session=session,        \n        database=working_database, \n        name=fs_schema,\n        default_warehouse=warehouse\n    )\nexcept:\n    # need privs to create fs if not exists\n    fs = FeatureStore(\n        session=session,        \n        database=working_database, \n        name=fs_schema, \n        default_warehouse=warehouse,\n        creation_mode=CreationMode.CREATE_IF_NOT_EXIST\n    )\n## define the primary or join keys \njoin_keys = [\"MEMBER_ID\", \"REF_MMYY\"]\n```\n\n\n- **Register the Feature Views**\n\nThis snippet shows how to define entities, which are used to register feature views in the Feature Store.\n\n\n```\ndef register_feature(fs, entity_nm, fv_version, feature_df, join_keys):\n    \"\"\"\n    Registers an entity and a feature view in the feature store if they do not already exist.\n\n    Parameters:\n        fs (FeatureStore): Feature store client instance\n        entity_nm (str): Name of the entity\n        fv_version (str): Version of the feature view\n        feature_df (DataFrame):  DataFrame containing feature data\n        join_keys (list): List of join keys for the entity\n\n    Returns:\n        FeatureView: The registered or retrieved FeatureView instance\n    \"\"\"\n\n    fv_name = f\"FV_FEATURE_{entity_nm}\"\n\n    # Check if entity exists\n    entity_names_json = fs.list_entities().select(F.to_json(F.array_agg(\"NAME\", True))).collect()[0][0]\n    existing_entities = json.loads(entity_names_json)\n\n    if entity_nm not in existing_entities:\n        entity_instance = Entity(name=entity_nm, join_keys=join_keys, desc=f\"Primary Keys for {entity_nm}\")\n        fs.register_entity(entity_instance)\n    else:\n        entity_instance = fs.get_entity(entity_nm)\n\n    # Try to get the FeatureView; register it if it doesn't exist\n    try:\n        fv_feature_instance = fs.get_feature_view(fv_name, fv_version)\n    except:\n        fv_feature_instance = FeatureView(\n            name=fv_name,\n            entities=[entity_instance],\n            feature_df=feature_df\n        )\n        fs.register_feature_view(fv_feature_instance, version=fv_version, block=True)\n\n    return fv_feature_instance\n```\n- **Snowflake Dataset Creation**\n\nTo proceed with feature engineering and dimensionality reduction, we first prepare our dataset.  \n[Datasets](https://docs.snowflake.com/en/developer-guide/snowflake-ml/dataset) are new Snowflake schema-level objects specifically designed for machine learning workflows.  \nSnowflake Datasets hold collections of data organized into versions.\n\n\n```\n#retrieve the entity views\n\nfv_feature_ent1_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_1\", \"V_1\")\nfv_feature_ent2_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_2\", \"V_1\")\nfv_feature_ent3_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_3\", \"V_1\")\nfv_feature_ent4_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_4\", \"V_1\")\n\n\nfv_list = [fv_feature_ent1_instance, \n           fv_feature_ent2_instance, \n           fv_feature_ent3_instance,\n           fv_feature_ent4_instance] \n\nds_cols = []\nslice_list = []\nfor fv in fv_list:\n    fv_cols = list(fv._feature_desc)\n    slice_cols = [col for col in fv_cols if col not in ds_cols]\n    #fv = fv.slice(slice_cols)\n    slice_list.append(fv.slice(slice_cols))\n    ds_cols += fv_cols\n\n ## create DS   \ndataset = fs.generate_dataset(\n    name=f\"{working_database}.{working_schema}.POC_DATASET\",\n    spine_df=universe_sdf,\n    features = slice_list,\n    version=\"V_1\",\n    output_type=\"table\",\n    spine_label_cols=[\"TARGET\"],\n    desc=\"training dataset for ml poc\"\n) \n```\n- **Check the Dataset Table**\n\nYou can check the contents of the dataset table **POC_DATASET** using the query below:\n\n\n```\nselect * from POC_DATASET;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Feature Reduction \n\nIn this step, we preprocess the dataset by performing feature reduction, removing redundant or irrelevant features before model training. For feature reduction, we will employ techniques such as Variance Threshold and Correlation Analysis. There are numerous other dimensionality reduction techniques available, which you can explore further [here](https://en.wikipedia.org/wiki/Dimensionality_reduction)\n\n### **Why to do Feature Reduction ?** \n\nFeature reduction simplifies models, reduces overfitting, and improves computational efficiency.\n\nTo complete this step, import the notebook [02_Feature_Reduction.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/02_Feature_Reduction.ipynb) into your Snowflake environment. Once imported, run all cells to execute the setup. \n\n\n### **Variance Threshold:**\n\nRemoves features with low variance that offer little predictive value, such as those with near-constant values across samples. Here's a code snippet from the notebook\n\n```\nfrom snowflake.snowpark import DataFrame\n#from snowflake.snowpark.functions import var_pop\n\n## get all columns with stringType= type\nexcluded = ['MEMBER_ID', 'TARGET','REF_MMYYYY','CAT_1','CAT_2','CAT_3','CAT_4','CAT_5']\nnum_cols = [col for col in sdf.columns if col not in excluded]\n\nsession.use_warehouse('SNOWPARK_OPT_W')\nprint(f'number of features before the variance threshold {len(num_cols)}')\n\n# get the\nvariance_df = sdf.select([F.var_pop(F.col(c)).alias(c) for c in num_cols])\n\nvariance_df = variance_df.to_pandas()\ncols_below_threshold  = variance_df.columns[(variance_df  \u003C 0.1).all()]\nprint( f\" total cols having variance threshold less than 0.1  is {len(cols_below_threshold)}\")\n\nsdf=sdf.drop(*cols_below_threshold )\n\nprint(f'number of features after applying the variance threshold  {len(cols_below_threshold)}')\n\n```\n\n### **Correlation Analysis:**\n \nIdentifies and removes highly correlated features (e.g., correlation \u003E 0.8), which provide redundant information and may lead to instability. Here's a code snippet from the notebook\n\n```\nfrom snowflake.ml.modeling.metrics.correlation import correlation\n\n\ndef snf_correlation_thresholder(df, features, corr_threshold: float):\n    assert 0 \u003C corr_threshold \u003C= 1, \"Correlation threshold must be in range (0, 1].\"\n    \n    corr_features = set()\n    corr_matrix = correlation(df=sdf)\n\n    # Compute pairwise correlations directly in Snowpark\n    for i in range(len(features)):\n        for j in range(i + 1, len(features)):\n            if (abs(corr_matrix.iloc[i][j])) \u003E=  corr_threshold:\n            #col1, col2 = features[i], features[j]\n            #corr_value = df.select(corr(col(col1), col(col2)).alias('corr')).collect()[0]['CORR']\n            \n           # if corr_value is not None and abs(corr_value) \u003E= corr_threshold:\n                # Mark the second feature for removal to avoid keeping highly correlated pairs\n                #corr_features.add(col2)\n                corr_features.add(features[j])\n    \n    # Drop correlated features if any\n    if corr_features:\n        df = df.drop(*corr_features)\n        \n    return df\n```\n\nFinally, we reduce the total number of columns in the dataset from 508 to 150. This refined dataset now serves as the input for the next step: model training. \n\n\u003C!-- ------------------------ --\u003E\n## Model Training \n\nIn this step, we will train the model using Snowflake ML, beginning by creating a preprocessing pipeline that will convert categorical variables into numerical format through one-hot encoding and apply Min-Max scaling to standardize numerical features.\n\nAfter preprocessing, we will experiment with several modeling techniques, including XGBoost and Random Forest. Model tuning will be conducted using GridSearch, and all training will be executed within Snowflake ML.\n\nTo complete this step, import the notebook [03_Model_Training.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/03_Model_Training.ipynb) into your Snowflake environment. Once imported, run all cells to execute the setup. \n\n\n### **Preprocessor Pipeline**\n\nHere’s the code snippet for the preprocessing pipeline.  \nThe pipeline saves the model as a `.joblib` file in a Snowflake stage for reusability.\n\n\n```\nfrom snowflake.ml.modeling.pipeline import Pipeline \nfrom snowflake.ml.modeling.preprocessing import MinMaxScaler , OneHotEncoder\n\npreprocessing_pipeline = Pipeline(\n    steps=[\n        \n        (\"OHE\",\n         OneHotEncoder(input_cols=cat_cols,\n                       output_cols=cat_cols,\n                       drop_input_cols=True,\n                       drop=\"first\",\n                       handle_unknown=\"ignore\",)\n         ),\n        (\"MMS\",MinMaxScaler(clip=True, \n                            input_cols=num_cols,\n                            output_cols=num_cols,))\n    ]\n\n)\n\njoblib.dump(preprocessing_pipeline, 'preprocessing_pipeline.joblib')\n#upload\nsession.file.put('preprocessing_pipeline.joblib',\n                 stage,auto_compress=False)\n```\n### **XGBoost Classifier in Snowflake ML**\n\nSnippet for training an XGBoost Classifier using Snowflake ML:\n\n```\nfrom snowflake.ml.modeling.xgboost import XGBClassifier\nXGB_Classifier= XGBClassifier(\n    input_cols=FEATURE_COLS ,\n    label_cols=label_col,\n    output_cols=OUTPUT_COLUMNS\n)\n# Train\nXGB_Classifier.fit(train_df)\n\n#  evaluation \npredict_on_training_data = XGB_Classifier.predict(train_df)\n\ntraining_accuracy = accuracy_score(df=predict_on_training_data, \n                                   y_true_col_names=[\"TARGET\"],\n                                   y_pred_col_names=[\"PREDICTED_TARGET\"])\n\n\nresult = XGB_Classifier.predict(test_df)\n```\n\n### **Random Forest Classifier in Snowflake ML**\n\nSnippet for training a Random Forest Classifier using Snowflake ML:\n```\nfrom snowflake.ml.modeling.ensemble import RandomForestClassifier\n\nFEATURE_COLS = get_features(train_df, label_col)\nOUTPUT_COLUMNS=\"PREDICTED_TARGET\"\nlabel_col='TARGET'\n\n\nRandomForest= RandomForestClassifier(\n    input_cols=FEATURE_COLS ,\n    label_cols=label_col,\n    output_cols=OUTPUT_COLUMNS\n)\n# Train\nRandomForest.fit(train_df)\n```\n### **Model Evaluation**\n\nRun predictions on the training dataset using the trained Random Forest model:\n\n```\npredict_on_training_data = RandomForest.predict(train_df)\n\n```\n### **Grid Search in Snowflake ML**\n\nSnippet for performing Grid Search hyperparameter tuning using Snowflake ML:\n\n```\n## parameter grid \nFEATURE_COLS = get_features(train_df, label_col)\nOUTPUT_COLUMNS=\"PREDICTED_TARGET\"\nlabel_col='TARGET'\n\n\n\nparameters = {\n        \"n_estimators\": [100, 300, 500],\n        \"learning_rate\": [0.1, 0.3, 0.5],\n        \"max_depth\": list(range(3, 5, 1)),\n        \"min_child_weight\": list(range(3, 5, 1)),\n    }\n    \nn_folds = 5\n\nestimator = XGBClassifier()\n\nGridSearch_clf = GridSearchCV(estimator= estimator,\n                   param_grid=parameters ,\n                   cv = n_folds,\n                   input_cols=FEATURE_COLS ,\n                   label_cols=label_col,\n                   output_cols=OUTPUT_COLUMNS\n                   )\nGridSearch_clf.fit(train_df)\n\nresult = GridSearch_clf.predict(test_df )\nprint(GridSearch_clf.to_sklearn().best_estimator_)\n```\n\n** Model Metrics **\nSnippet showing how to get the model metrics natively using Snowflake ML\n\n```\n# snowpark ML metrics\nfrom snowflake.ml.modeling.metrics import accuracy_score,f1_score,precision_score,roc_auc_score,roc_curve,recall_score\n\nmetrics = {\n\"accuracy\":accuracy_score(df=result ,\n                          y_true_col_names=\"TARGET\", \n                          y_pred_col_names=\"PREDICTED_TARGET\"),\n\n\"precision\":precision_score(df=result,\n                            y_true_col_names=\"TARGET\", \n                            y_pred_col_names=\"PREDICTED_TARGET\"),\n\n\n\"recall\": recall_score(df=result, \n                       y_true_col_names=\"TARGET\",\n                       y_pred_col_names=\"PREDICTED_TARGET\"),\n\n\n\n\"f1_score\":f1_score(df=result,\n                   y_true_col_names=\"TARGET\",\n                   y_pred_col_names=\"PREDICTED_TARGET\"),\n\"confusion_matrix\":confusion_matrix(df=result, \n                                    y_true_col_name=\"TARGET\",\n                                    y_pred_col_name=\"PREDICTED_TARGET\").tolist()\n}\n\n```\n\n**Loging Model into Model registry**\nSnippet to register a model in Snowflake ML\n\n```\n# Get sample input data to pass into the registry logging function\nX = train_df.select(FEATURE_COLS).limit(100)\ndb = working_database \nschema =model_registry_schema \n\n# Create a registry and log the model\nreg = Registry(session=session, database_name=db, schema_name=schema)\n\n\nmodel_name = \"ML_XGBOOST_MODEL\"\nversion_name = \"v1\"\n\n# Let's first log the very first model we trained\nmv = reg.log_model(\n    model_name=model_name,\n    version_name=version_name,\n    model= XGB_Classifier,\n    metrics=metrics ,\n    sample_input_data=X, # to provide the feature schema\n)\n\n\n# Add a description\nmv.comment = \"\"\"This is the first iteration of our ml poc  model. \nIt is used for demo purposes and it is simple xgboost model.\"\"\"\n\n\n# Let's confirm they were added\nreg.get_model(model_name).show_versions()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Model Inferencing and Scheduling\n\nIn this section, you'll learn how to perform batch inferencing using Snowflake ML by:\n* Leveraging features stored in the Feature Store \n* Loading the model signature from the Snowflake Model Registry \n* Running predictions at scale within Snowflake \n\nThis approach enables efficient generation of model predictions for large datasets, all within the Snowflake platform. Additionally, the notebook can be [scheduled](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-schedule) to run at regular intervals, facilitating fully automated and production-grade batch scoring workflows.\n\nTo proceed, please import the notebook [04_Batch_Inferencing.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/blob/main/04_Batch_Inferencing.ipynb) and run all the cells. Let’s now dive into the code details.\n\n### 1. Retrieve Features from the Feature Store\n\nRetrieve features from the Feature Store using the model signature of the selected model from the Model Registry.\n\nBelow is the code snippet from the notebook:\n\n```\nfv_feature_ent1_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_1\", \"V_1\")\nfv_feature_ent2_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_2\", \"V_1\")\nfv_feature_ent3_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_3\", \"V_1\")\nfv_feature_ent4_instance  = fs.get_feature_view(\"FV_FEATURE_ENT_4\", \"V_1\")\n\n\nfv_list = [fv_feature_ent1_instance, \n           fv_feature_ent2_instance, \n           fv_feature_ent3_instance,\n           fv_feature_ent4_instance] \n\n\nuniverse_tbl = '.'.join([input_database, input_schema, 'DEMO_TARGETS_TBL'])\nuniverse_sdf            = session.table(universe_tbl).filter(F.col(\"REF_MMYY\") == ref_mmyyyy)\n\n\n#get the input signature from the desired model from the model registr\n\nreg = Registry(session, database_name = working_database,schema_name = model_registry_schema)\nreg.show_models()\nmv = reg.get_model(model_name).version(\"v1\")\n# the input signature of model\ninput_signature = mv.show_functions()[0].get(\"signature\").inputs\ninput_cols = [c.name for c in input_signature]\n```\n### 2 Create Dataset for Inference\n\nSnippet of code to generate the dataset used for inference:\n```\ndataset = fs.generate_dataset(\n    name=f\"{working_database}.{working_schema}.INFERENCE_DATASET\",\n    spine_df=universe_sdf,\n    features = slice_list,\n    version=dataset_version,\n    #output_type=\"table\",\n    spine_label_cols=[\"TARGET\"],\n    desc=\"training dataset for ml demo\"\n)    \n```\n### 3. Run Preprocessing\n\nSnippet of code to run preprocessing on unseen or inference data:\n```\nsession.file.get(f'{stage}/preprocessing_pipeline.joblib', '/tmp')\nPIPELINE_FILE = '/tmp/preprocessing_pipeline.joblib'\n\npreprocessing_pipeline = joblib.load(PIPELINE_FILE)\n\ndf=preprocessing_pipeline.fit(df).transform(df)\n```\n### 3. Run Prediction\n\nSnippet of code to run prediction on an unseen or prediction dataset:\n```\nprediction_result = mv.run(df, function_name =\"PREDICT\")\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You’ve successfully built an end-to-end customer targeting model in a Snowflake Notebook and logged the trained model to the Snowflake ML Registry, making it ready for inference and future use.\n\n### What You Learned\n- How to generate realistic synthetic data in Snowpark and save it as Snowflake tables.\n- How to define entities and register feature views in the Feature Store.\n- How to perform feature engineering and feature reduction.\n- How to train and tune models within Snowflake ML.\n- How to log and register models in the Snowflake ML registry.\n- How to run predictions on new data using the registered model within Snowflake.\n\n\n\n### Related Resources\n- [GitHub Repo](https://github.com/Snowflake-Labs/sfguide-getting-started-with-end-to-end-customer-targeting-on-snowflake-ml/tree/main)\n- [Snowflake Logging Custom Models](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/bring-your-own-model-types)\n- [Snowflake ML](/en/data-cloud/snowflake-ml/)\n- [classification Model](https://en.wikipedia.org/wiki/Category:Classification_algorithms)\n\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-5a3c9512b3","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-938d2cb9a8","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-b989ce2b55","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-4fec5e210e","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. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"id":"container-ab437aa28b","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"id":"container-f449d9ec3b","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"id":"container-bc2de563d5","layout":"SIMPLE","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-272d37a023",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/getting-started-with-e2e-customer-targeting-with-snowflake-ml","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESetup\u003C/h2\u003E","\u003Ch2\u003EGenerate Realistic Synthetic Data\u003C/h2\u003E","\u003Ch2\u003EFeature Store Creation in Snowflake\u003C/h2\u003E","\u003Ch2\u003EFeature Reduction\u003C/h2\u003E","\u003Ch2\u003EModel Training\u003C/h2\u003E","\u003Ch2\u003EModel Inferencing and Scheduling\u003C/h2\u003E","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-dbc21e6943",":type":"snowflake-site/components/quickstart/quickstart-button","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/getting-started-with-e2e-customer-targeting-with-snowflake-ml","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-3d9f041217","title":"Page 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