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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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-240389df57","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Getting Started with Model Serving in Snowpark Container 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Snowpark Container Services (SPCS) and the Snowflake Model Registry to build, deploy, and serve a machine learning model for real-time predictions in Snowflake ML. We will walk through a complete data science workflow, from data preparation and feature engineering to model training and deployment as a secure, scalable service.\u003C/p\u003E\n","\u003Cp\u003EThis guide uses a real-world dataset of Chicago Transit Authority (CTA) daily ridership to forecast future demand. By the end, you will have a fully functional model serving endpoint accessible from both within and outside of Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up your Snowflake environment for machine learning.\u003C/li\u003E\u003Cli\u003EHow to perform feature engineering with Snowpark.\u003C/li\u003E\u003Cli\u003EHow to train, register, and version a model using the Snowflake Model Registry.\u003C/li\u003E\u003Cli\u003EHow to deploy a model as a service in Snowpark Container Services.\u003C/li\u003E\u003Cli\u003EHow to access the deployed model endpoint using both Key-Pair and Programmatic Access Token (PAT) authentication.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA complete data pipeline for feature engineering.\u003C/li\u003E\u003Cli\u003EA trained XGBoost regression model to forecast bus ridership.\u003C/li\u003E\u003Cli\u003EA secure, scalable model serving endpoint hosted in Snowpark Container Services.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Python and SQL.\u003C/li\u003E\u003Cli\u003EBasic understanding of machine learning concepts.\u003C/li\u003E\u003Cli\u003ENote that, trial account does not allow creation of compute pool, so model serving on Container Services cannot be tested in Trial Account.\u003C/li\u003E\u003Cli\u003EHave a database schema where objects can be created. In this exercise, we will create a notebook, table to store data, stored proc for training, model to log the trained model, container images for model serving and the service hosting the model. You need privileges to have all of them.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Setup\u003C/h2\u003E\n","\u003Cp\u003EFirst, we will create a database called \u003Cstrong\u003EMODEL_SERVING_DB\u003C/strong\u003E in Snowflake as below. We will also create a warehouse called \u003Cstrong\u003EDEMO_BUILD_WAREHOUSE\u003C/strong\u003E.  If you do not have privileges to create a database or warehouse, feel free to use existing ones where you have permission (please note to change database schema name accordingly throughout this exercise).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/1_DB_Creation.png\" alt=\"1_DB_Creation\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will then import the attached notebook inside this github \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-serving-in-spcs\"\u003Erepo\u003C/a\u003E by clicking the import notebook option in Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/2_Import_Notebooks.png\" alt=\"2_Import_Notebooks\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName: \u003Cstrong\u003ESNOWPARK_FORECASTING_MODEL_DEPLOYMENT\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ENotebook location: Set to the \u003Cstrong\u003E"MODEL_SERVING_DB"\u003C/strong\u003E database and the \u003Cstrong\u003E"PUBLIC"\u003C/strong\u003E schema.\u003C/li\u003E\u003Cli\u003EPython environment: \u003Cstrong\u003E"Run on warehouse"\u003C/strong\u003E is selected, which is described as best for data analysis. The alternative, \u003Cstrong\u003E"Run on container"\u003C/strong\u003E is for AI/ML workloads. In this case we will choose Run on container and we will choose the default compute pool.\u003C/li\u003E\u003Cli\u003EQuery warehouse: This is the name of the warehouse used to run our queries. In this case I have created a warehouse called \u003Cstrong\u003EDEMO_BUILD_WH\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWe will also import the CSV file into the stage. This can be found in the github \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-serving-in-spcs\"\u003Erepo\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/3_Import_CSVs.png\" alt=\"3_Import_CSVs\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELastly, we will import our packages into Snowflake\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Esnowflake: Latest\u003C/li\u003E\u003Cli\u003Esnowflake-ml-python: Latest\u003C/li\u003E\u003Cli\u003Esnowflake.core: Latest\u003C/li\u003E\u003Cli\u003Epython: 3.9.*\u003C/li\u003E\u003Cli\u003Estreamlit: 1.39.1\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_Import_Packages.png\" alt=\"4_Import_Packages\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESetup Environment\u003C/h3\u003E\n","\u003Cp\u003EFirst, we set up the Python environment by importing the necessary libraries. This includes Snowpark for data manipulation, Snowflake ML for modeling, and the Feature Store.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Snowpark for Python\nfrom snowflake.snowpark.session import Session\nfrom snowflake.snowpark.version import VERSION\nfrom snowflake.snowpark import functions as F\n\n# Snowpark ML\nfrom snowflake.ml.modeling.pipeline import Pipeline\nfrom snowflake.ml.modeling.xgboost import XGBRegressor\nfrom snowflake.ml.feature_store import FeatureStore, CreationMode, Entity, FeatureView\nfrom snowflake.ml.registry import Registry\n\n# Misc\nimport pandas as pd\n\n# Get active session\nsession = get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoad Data\u003C/h3\u003E\n","\u003Cp\u003EWe begin by loading the Chicago bus ridership data from a CSV file into a Pandas DataFrame, and then into a Snowpark DataFrame. This data is then saved as a table in Snowflake\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Read data and create Snowpark DataFrame\ndf_clean = pd.read_csv('CTA_Daily_Totals_by_Route.csv')\ndf_clean.columns = df_clean.columns.str.upper()\ninput_df = session.create_dataframe(df_clean)\n\n# Save DataFrame as a table in Snowflake\ninput_df.write.mode('overwrite').save_as_table('MODEL_SERVING_DB.FEATURE_STORE_MLDEMO.CHICAGO_BUS_RIDES')\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMachine Learning\u003C/h2\u003E\n","\u003Ch3\u003ECreate and Register a Feature Store\u003C/h3\u003E\n","\u003Cp\u003EA Feature Store is created to manage and version our features. We then register an Entity which represents a real-world object that our features are associated with, in this case, a bus route.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Create a feature store\nfs = FeatureStore(\n    session=session,\n    database="MODEL_SERVING_DB",\n    name="FEATURE_STORE_MLDEMO",\n    default_warehouse="DEMO_BUILD_WH",\n    creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\n)\n\n# Register an entity\nentity = Entity(\n    name="route",\n    join_keys=["DATE"],\n)\nfs.register_entity(entity)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDistributed Feature Engineering\u003C/h3\u003E\n","\u003Cp\u003EWe perform feature engineering to create new features that will be useful for our model. This includes extracting the day of the week and month from the date, and calculating the previous day's ridership. These engineered features are then registered as a FeatureView.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Perform feature engineering\nfrom snowflake.snowpark.functions import col, to_timestamp, dayofweek, month, sum, lag\nfrom snowflake.snowpark import Window\n\ndf = session.read.table('MODEL_SERVING_DB.FEATURE_STORE_MLDEMO.CHICAGO_BUS_RIDES')\ndf = df.with_column('DATE', to_timestamp(col('DATE'), 'MM/DD/YYYY'))\ndf = df.with_column('DAY_OF_WEEK', dayofweek(col('DATE')))\ndf = df.with_column('MONTH', month(col('DATE')))\n\ntotal_riders = df.group_by('DATE','DAY_OF_WEEK','MONTH').agg(\n    F.listagg('DAYTYPE', is_distinct=True).alias('DAYTYPE'),\n    F.sum('RIDES').alias('TOTAL_RIDERS')\n).order_by('DATE')\n\nwindow_spec = Window.order_by('DATE')\ntotal_riders = total_riders.with_column('PREV_DAY_RIDERS', lag(col('TOTAL_RIDERS'), 1).over(window_spec))\n\n# Register the feature view\nagg_fv = FeatureView(\n    name="AggBusData",\n    entities=[entity],\n    feature_df=total_riders,\n    timestamp_col="DATE",\n)\nagg_fv = fs.register_feature_view(agg_fv, version="1", overwrite=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EModel Training and Evaluation\u003C/h3\u003E\n","\u003Cp\u003EWith our features ready, we define a machine learning pipeline to preprocess the data and train an XGBoost Regressor model. The model is trained and evaluated in a distributed manner using Snowflake's compute resources.\u003C/p\u003E\n","\u003Cp\u003ECreate and Train the Model\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.modeling.compose import ColumnTransformer\nfrom snowflake.ml.modeling.preprocessing import StandardScaler, OrdinalEncoder\nfrom snowflake.ml.modeling.pipeline import Pipeline\nfrom snowflake.ml.modeling.xgboost import XGBRegressor\nfrom snowflake.ml.modeling.model_selection import GridSearchCV\n\n# Define preprocessing steps\nnumeric_features = ['DAY_OF_WEEK','MONTH','PREV_DAY_RIDERS','MINIMUM_TEMPERATURE','MAXIMUM_TEMPERATURE','PRECIPITATION']\nnumeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])\n\ncategorical_cols = ['DAYTYPE']\ncategorical_transformer = Pipeline(steps=[('onehot', OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-99999))])\n\npreprocessor = ColumnTransformer(\n    transformers=[\n        ('num', numeric_transformer, numeric_features),\n        ('cat', categorical_transformer, categorical_cols)\n        ])\n\npipeline = Pipeline(steps=[('preprocessor', preprocessor),('model', XGBRegressor())])\n\n# Use GridSearchCV for hyperparameter tuning\nhyper_param = dict(model__max_depth=[2,4], model__learning_rate=[0.1,0.3])\nxg_model = GridSearchCV(\n    estimator=pipeline,\n    param_grid=hyper_param,\n    input_cols=numeric_features + categorical_cols,\n    label_cols=['TOTAL_RIDERS'],\n    output_cols=["TOTAL_RIDERS_FORECAST"],\n)\n\n# Fit the model\nxg_model.fit(train)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EModel Lineage\u003C/h3\u003E\n","\u003Cp\u003ESnowflake’s UI provides a graphical representation of the lineage, making it easier to visualize and communicate your findings. To do so, we can go to the CHICAGO_BUS_RIDES_FORECAST table and click on lineage\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_lineage.png\" alt=\"4_lineage\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EModel Deployment\u003C/h2\u003E\n","\u003Ch3\u003EEvaluate the Model\u003C/h3\u003E\n","\u003Cp\u003EAfter training, we evaluate the model's performance on the test set.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.ml.modeling.metrics import mean_absolute_error\n\ntestpreds = xg_model.predict(test)\nprint('MSE:', mean_absolute_error(df=testpreds, y_true_col_names='TOTAL_RIDERS', y_pred_col_names='"TOTAL_RIDERS_FORECAST"'))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EModel Registration and Deployment\u003C/h3\u003E\n","\u003Cp\u003EFinally, the trained model is logged to the Snowflake Model Registry. This allows for versioning, management, and easy deployment of the model.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ereg_model = reg.log_model(\n    model_name="Forecasting_Bus_Ridership",\n    version_name="v2",    \n    model=xg_model,\n    target_platforms=["WAREHOUSE"],\n    conda_dependencies=["scikit-learn","xgboost"],\n    sample_input_data=train,\n    comment="My awesome XGBoost model"\n)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are able to see the model stored inside the Model tab of the database.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_model_served.png\" alt=\"4_model_served\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClicking on the model, we can then further drill down to understand the model such as the model lineage.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_lineage_model.png\" alt=\"4_lineage_model\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EDeploy to Snowpark Container Services (SPCS)\u003C/h3\u003E\n","\u003Cp\u003EThe registered model can be deployed as a service in SPCS for real-time inference.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ereg_model = reg.get_model("Forecasting_Bus_Ridership").version("v2")\n\nreg_model.create_service(service_name="ChicagoBusForecastv12",\n                  service_compute_pool="mypool",\n                  ingress_enabled=True)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESeeing the Inference endpoint\u003C/h3\u003E\n","\u003Cp\u003ETo see our inference endpoint, we can go to our model and click on the Inference Services tab. Opening the details, we can futher view the logs of our inference service\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_inference_serving.png\" alt=\"4_inference_serving\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EModel Serving\u003C/h2\u003E\n","\u003Cp\u003EThe Snowflake Model Registry, when used with containers, allows you to package, manage, and deploy machine learning models with complex dependencies or custom runtime environments inside Snowflake. This is accomplished by using Snowpark Container Services (SPCS) which we have done above. Some parts of the architecture for \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container\"\u003Emodel serving\u003C/a\u003E are as described below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/5_architecture.png\" alt=\"5_architecture\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInference Server: This is the engine that runs the model to serve predictions. It can use multiple processes to maximize the node's resources and includes an admission control feature to manage incoming requests, preventing overloads. Currently, Snowflake offers a flexible Python-based server, with plans to introduce servers optimized for specific model types in the future.\u003C/li\u003E\u003Cli\u003EModel-specific Python environment: To minimize the startup time for a model, Snowflake creates a container that packages the model with all its specific dependencies.\u003C/li\u003E\u003Cli\u003EService functions: These functions allow you to call the inference server from code running within a Snowflake warehouse. They are designed with the same signature as the original model and use the external function protocol to communicate with the server.\u003C/li\u003E\u003Cli\u003EIngress endpoint: An optional HTTP endpoint can be created, making the model accessible to applications outside of Snowflake over the public internet. This is achieved when we did the create service in the previous section.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOur model is now deployed and ready to serve predictions. The service endpoint is secured and requires authentication. We will explore two common methods for accessing it: Key-Pair Authentication for service accounts and Programmatic Access Tokens (PAT) for user-centric access.\u003C/p\u003E\n","\u003Cp\u003EFirst, you need the service's ingress URL. You can find it by running the following SQL command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW ENDPOINTS IN SERVICE MODEL_SERVING_DB.FEATURE_STORE_MLDEMO.CHICAGOBUSFORECASTV12\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe full prediction endpoint will be\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ehttps://<YOUR_INGRESS_PATH>/predict\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Epredict here comes from the function exposed by the model. To know all the functions exposed in a model, you can use:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW FUNCTIONS IN MODEL Forecasting_Bus_Ridership VERSION v2;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUsing Key-Pair Authentication\u003C/h3\u003E\n","\u003Cp\u003EThis method is ideal for applications and service accounts. It involves using a public/private key pair to generate a session token for authentication. We will set up our SnowSQL connection and have a named connection in your \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-session#connect-by-using-the-connections-toml-file\"\u003Econnections.toml\u003C/a\u003E. In this example our named connection is called \u003Cstrong\u003E"named_connection"\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E[named_connection]\n#Can be used in SnowSql as #connect example\naccountname = XXX\nusername = YYY\nrole = myrole\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will be passing in some dummy data and transforming the DataFrame into a JSON-like format before passing it to our predict URL endpoint. The full code can be found below.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etest = [[6 ,7,"A",486139,734535,"USW00014819",22.8,34.4,3.6 ,'2019-07-13'],  \n[6 ,6,"A",444588,784734,"USW00014819",13.9,21.7,5.1 ,'2019-06-15'],\n[4 ,6,"W",780768,765409,"USW00014819",11.1,20.6,0.5 ,'2019-06-13']]\n\ndf = pd.DataFrame(test, columns=['DAY_OF_WEEK' ,'MONTH' ,'DAYTYPE' ,'TOTAL_RIDERS' ,'PREV_DAY_RIDERS' ,'NOAA_WEATHER_STATION_ID' ,'MINIMUM_TEMPERATURE' ,'MAXIMUM_TEMPERATURE' ,'PRECIPITATION' ,'DATE'])\n\ndata = {"data": np.column_stack([range(df.shape[0]), df.values]).tolist()}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce done, save the code inside a file called "KP-app.py" and do "\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Epython KP-app.py\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport json\nfrom pprint import pprint\nimport requests\nimport snowflake.connector\nfrom snowflake.ml.utils.connection_params import SnowflakeLoginOptions\nimport pandas as pd\nimport numpy as np\n\n# --- 1. Connection and Authentication ---\n# This part of your code is correct. It gets a valid token.\ndef initiate_snowflake_connection():\n    # IMPORTANT: Ensure "named_connection" is correctly defined in your\n    # ~/.snowflake/connections.toml file.\n    connection_parameters = SnowflakeLoginOptions("named_connection")\n    connection_parameters["session_parameters"] = {"PYTHON_CONNECTOR_QUERY_RESULT_FORMAT": "json"}\n    connection_parameters["insecure_mode"] = True  # Disable SSL verification\n    snowflake_conn = snowflake.connector.connect(**connection_parameters)\n    return snowflake_conn\n\ndef get_headers(snowflake_conn):\n    token = snowflake_conn._rest._token_request('ISSUE')\n    headers = {'Authorization': f'Snowflake Token="{token["data"]["sessionToken"]}"'}\n    return headers\n\nprint("Connecting to Snowflake to get auth token...")\nheaders = get_headers(initiate_snowflake_connection())\nprint("Token acquired successfully.")\n\n# --- 2. Endpoint URL ---\n# This must be the public endpoint for your service.\nURL = 'https://<YOUR_INGRESS_PATH>/predict'\n\n# --- 3. Preparing our data ---\ndef prepare_data():\n    # Ensure 'test' is defined before this point\n    # Assuming 'test' is a Snowpark DataFrame object\n    test = [[6 ,7,"A",486139,734535,"USW00014819",22.8,34.4,3.6 ,'2019-07-13'],  \n[6 ,6,"A",444588,784734,"USW00014819",13.9,21.7,5.1 ,'2019-06-15'],\n[4 ,6,"W",780768,765409,"USW00014819",11.1,20.6,0.5 ,'2019-06-13']]\n\n    df = pd.DataFrame(test, columns=['DAY_OF_WEEK' ,'MONTH' ,'DAYTYPE' ,'TOTAL_RIDERS' ,'PREV_DAY_RIDERS' ,'NOAA_WEATHER_STATION_ID' ,'MINIMUM_TEMPERATURE' ,'MAXIMUM_TEMPERATURE' ,'PRECIPITATION' ,'DATE'])\n    print(df)\n\n    data = {"data": np.column_stack([range(df.shape[0]), df.values]).tolist()}     \n    print(data)\n    return data\n\nsample_df = prepare_data()\n\n\n# --- 4. Sending the Request ---\ndef send_request(data: dict, headers: dict):\n    """Posts the data to the URL and handles the response."""\n    print("nSending request to endpoint...")\n    try:\n        output = requests.post(URL, json=sample_df, headers=headers)\n        \n        print("--- Response from Server ---")\n        print(f"Status Code: {output.status_code}")\n        \n        # Try to print JSON if possible, otherwise print raw text\n        try:\n            response_json = output.json()\n            print("Response JSON:")\n            pprint(response_json)\n        except json.JSONDecodeError:\n            print("Raw Response Text:")\n            print(output.text)\n            \n        return output\n\n    except requests.exceptions.RequestException as e:\n        print(f"An error occurred during the request: {e}")\n        return None\n\n# --- Run the request ---\nsend_request(data=sample_df, headers=headers)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EUsing Programmatic Access Token (PAT)\u003C/h3\u003E\n","\u003Cp\u003EPATs are excellent for interactive or user-driven scenarios. A user generates a token in the Snowsight UI and uses it to authenticate.\u003C/p\u003E\n","\u003Cp\u003ESetup:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESign in to Snowsight.\u003C/li\u003E\u003Cli\u003EClick on your username in the top left corner, then select My Profile.\u003C/li\u003E\u003Cli\u003EIn the dialog that appears, scroll down to the Programmatic Access Tokens section.\u003C/li\u003E\u003Cli\u003EClick + Generate new token.\u003C/li\u003E\u003Cli\u003EFill in the token details:\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EName: A descriptive name for your token (e.g., dbt_cloud_token).\u003C/li\u003E\u003Cli\u003EExpires in: Choose an expiration period (e.g., 90 days). It is a security best practice to set an expiration date.\u003C/li\u003E\u003Cli\u003ERole: You can restrict the token to a specific role. This is highly recommended to limit the token's permissions to only what is necessary.\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EClick Generate.\u003C/li\u003E\u003Cli\u003EA dialog box will display your new token. This is the only time the token secret will be shown. Copy it immediately.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/6_PAT.png\" alt=\"6_PAT\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce done, save the code below inside a file called "PAT-app.py" and do\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Epython PAT-app.py \n  --account <ACCOUNT_IDENTIFIER> \n  --user <USERNAME>> \n  --role ACCOUNTADMIN \n  --pat <PASS_IN_YOUR_PAT_TOKEN>\n  --endpoint <YOUR_INGRESS_PATH> \n  --endpoint-path /predict\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom datetime import timedelta\nimport argparse\nimport logging\nimport sys\nimport requests\nimport numpy as np\nfrom pprint import pprint\nimport json\nimport pandas as pd\n\nlogger = logging.getLogger(__name__)\n\ndef main():\n    args = _parse_args()\n    spcs_access_token = token_exchange_pat(args.pat, endpoint=args.endpoint,\n           role=args.role,\n           snowflake_account_url=args.snowflake_account_url, \n      snowflake_account=args.account)\n    spcs_url=f'https://{args.endpoint}{args.endpoint_path}'\n    connect_to_spcs(spcs_access_token, spcs_url)\n\ndef token_exchange_pat(pat, role, endpoint, snowflake_account_url, snowflake_account) -> str:\n    scope_role = f'session:role:{role}' if role is not None else None\n    scope = f'{scope_role} {endpoint}' if scope_role is not None else endpoint\n\n    data = {\n        'grant_type': 'urn:ietf:params:oauth:grant-type:token-exchange',\n        'scope': scope,\n        'subject_token': pat,\n        'subject_token_type': 'programmatic_access_token'\n    }\n    logger.info(data)\n    url = f'https://{snowflake_account}.snowflakecomputing.com/oauth/token'\n    if snowflake_account_url:\n        url = f'{snowflake_account_url}/oauth/token'\n    response = requests.post(url, data=data)\n    logger.info("snowflake token : %s" % response.text)\n    logger.info("snowflake token response code : %s" % response.status_code)\n    assert 200 == response.status_code, "unable to get snowflake token"\n    return response.text\n\n\ndef prepare_data():\n    # Ensure 'test' is defined before this point\n    # Assuming 'test' is a Snowpark DataFrame object\n    test = [[6 ,7,"A",486139,734535,"USW00014819",22.8,34.4,3.6 ,'2019-07-13'],  \n[6 ,6,"A",444588,784734,"USW00014819",13.9,21.7,5.1 ,'2019-06-15'],\n[4 ,6,"W",780768,765409,"USW00014819",11.1,20.6,0.5 ,'2019-06-13']]\n\n    df = pd.DataFrame(test, columns=['DAY_OF_WEEK' ,'MONTH' ,'DAYTYPE' ,'TOTAL_RIDERS' ,'PREV_DAY_RIDERS' ,'NOAA_WEATHER_STATION_ID' ,'MINIMUM_TEMPERATURE' ,'MAXIMUM_TEMPERATURE' ,'PRECIPITATION' ,'DATE'])\n    print(df)\n\n    data = {"data": np.column_stack([range(df.shape[0]), df.values]).tolist()}     \n    print(data)\n    return data\n       \n\ndef connect_to_spcs(token, url):\n  # Create a request to the ingress endpoint with authz.\n  headers = {'Authorization': f'Snowflake Token="{token}"'}\n  logger.info(url)\n  request_body = prepare_data()  # Convert the DataFrame to the right format\n  print (request_body)\n  response = requests.post(f'{url}',data=json.dumps(request_body), headers=headers)     #data=json.dumps(request_body)\n  #assert (response.status_code == 200), f"Failed to get response from the service. Status code: {response.status_code}"\n  #return response.content\n  logger.info("return code %s" % response.status_code)\n  logger.info(response.text)\n  logger.info(response.content)\n  logger.debug(response.content)\n\ndef _parse_args():\n  logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n  cli_parser = argparse.ArgumentParser()\n  cli_parser.add_argument('--account', required=True,\n              help='The account identifier (for example, "myorganization-myaccount" for '\n                '"myorganization-myaccount.snowflakecomputing.com").')\n  cli_parser.add_argument('--user', required=True, help='The user name.')\n  cli_parser.add_argument('--role',\n              help='The role we want to use to create and maintain a session for. If a role is not provided, '\n                'use the default role.')\n  cli_parser.add_argument('--endpoint', required=True,\n              help='The ingress endpoint of the service')\n  cli_parser.add_argument('--endpoint-path', default='/',\n              help='The url path for the ingress endpoint of the service')\n  cli_parser.add_argument('--snowflake_account_url', default=None,\n              help='The account url of the account for which we want to log in. Type of '\n                'https://myorganization-myaccount.snowflakecomputing.com')\n  cli_parser.add_argument('--pat',\n                            help='PAT for the user.')\n  args = cli_parser.parse_args()\n  return args\n\n\nif __name__ == "__main__":\n    main()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf all is well you should get a response like below\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/7_response.png\" alt=\"7_response\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You have successfully built, deployed, and accessed a machine learning model using Snowpark Container Services. You've seen how \u003Ca href=\"/en/product/use-cases/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E can manage the entire MLOps lifecycle within a single, unified platform, from data engineering to production deployment.\u003C/p\u003E\n","\u003Cp\u003EThis powerful combination of Snowpark, the Model Registry, and SPCS enables data teams to build and scale AI/ML applications securely and efficiently.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESetting up a complete ML environment in Snowflake.\u003C/li\u003E\u003Cli\u003ETraining and versioning models with the Snowflake Model Registry.\u003C/li\u003E\u003Cli\u003EDeploying models as scalable services using SPCS.\u003C/li\u003E\u003Cli\u003EAuthenticating and consuming model predictions from a secure endpoint.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/en/product/use-cases/end-to-end-ml-workflows/\"\u003ESnowflake ML homepage\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart\"\u003EMore ML Quickstart\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container\"\u003EModel Serving in SPCS\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-serving-in-spcs\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with Model Serving in Snowpark Container Services",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview\r\n\r\nIn this guide, you will learn how to leverage the power of Snowpark Container Services (SPCS) and the Snowflake Model Registry to build, deploy, and serve a machine learning model for real-time predictions in Snowflake ML. We will walk through a complete data science workflow, from data preparation and feature engineering to model training and deployment as a secure, scalable service.\r\n\r\nThis guide uses a real-world dataset of Chicago Transit Authority (CTA) daily ridership to forecast future demand. By the end, you will have a fully functional model serving endpoint accessible from both within and outside of Snowflake.\r\n\r\n### What You Will Learn\r\n-   How to set up your Snowflake environment for machine learning.\r\n-   How to perform feature engineering with Snowpark.\r\n-   How to train, register, and version a model using the Snowflake Model Registry.\r\n-   How to deploy a model as a service in Snowpark Container Services.\r\n-   How to access the deployed model endpoint using both Key-Pair and Programmatic Access Token (PAT) authentication.\r\n\r\n### What You Will Build\r\n-   A complete data pipeline for feature engineering.\r\n-   A trained XGBoost regression model to forecast bus ridership.\r\n-   A secure, scalable model serving endpoint hosted in Snowpark Container Services.\r\n\r\n### Prerequisites\r\n-   Familiarity with Python and SQL.\r\n-   Basic understanding of machine learning concepts.\r\n-   Note that, trial account does not allow creation of compute pool, so model serving on Container Services cannot be tested in Trial Account.\r\n- Have a database schema where objects can be created. In this exercise, we will create a notebook, table to store data, stored proc for training, model to log the trained model, container images for model serving and the service hosting the model. You need privileges to have all of them. \r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Snowflake Setup\r\n\r\nFirst, we will create a database called **MODEL_SERVING_DB** in Snowflake as below. We will also create a warehouse called **DEMO_BUILD_WAREHOUSE**.  If you do not have privileges to create a database or warehouse, feel free to use existing ones where you have permission (please note to change database schema name accordingly throughout this exercise).\r\n\r\n![1_DB_Creation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/1_DB_Creation.png)\r\n\r\nWe will then import the attached notebook inside this github [repo](https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-serving-in-spcs) by clicking the import notebook option in Snowflake. \r\n\r\n![2_Import_Notebooks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/2_Import_Notebooks.png)\r\n\r\n- Name: **SNOWPARK_FORECASTING_MODEL_DEPLOYMENT**\r\n- Notebook location: Set to the **\"MODEL_SERVING_DB\"** database and the **\"PUBLIC\"** schema.\r\n- Python environment: **\"Run on warehouse\"** is selected, which is described as best for data analysis. The alternative, **\"Run on container\"** is for AI/ML workloads. In this case we will choose Run on container and we will choose the default compute pool. \r\n- Query warehouse: This is the name of the warehouse used to run our queries. In this case I have created a warehouse called **DEMO_BUILD_WH**.\r\n\r\nWe will also import the CSV file into the stage. This can be found in the github [repo](https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-serving-in-spcs) \r\n\r\n![3_Import_CSVs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/3_Import_CSVs.png)\r\n\r\nLastly, we will import our packages into Snowflake\r\n\r\n- snowflake: Latest\r\n- snowflake-ml-python: Latest\r\n- snowflake.core: Latest\r\n- python: 3.9.*\r\n- streamlit: 1.39.1\r\n\r\n![4_Import_Packages](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_Import_Packages.png)\r\n\r\n### Setup Environment\r\nFirst, we set up the Python environment by importing the necessary libraries. This includes Snowpark for data manipulation, Snowflake ML for modeling, and the Feature Store.\r\n\r\n```python\r\n# Snowpark for Python\r\nfrom snowflake.snowpark.session import Session\r\nfrom snowflake.snowpark.version import VERSION\r\nfrom snowflake.snowpark import functions as F\r\n\r\n# Snowpark ML\r\nfrom snowflake.ml.modeling.pipeline import Pipeline\r\nfrom snowflake.ml.modeling.xgboost import XGBRegressor\r\nfrom snowflake.ml.feature_store import FeatureStore, CreationMode, Entity, FeatureView\r\nfrom snowflake.ml.registry import Registry\r\n\r\n# Misc\r\nimport pandas as pd\r\n\r\n# Get active session\r\nsession = get_active_session()\r\n```\r\n\r\n### Load Data \r\nWe begin by loading the Chicago bus ridership data from a CSV file into a Pandas DataFrame, and then into a Snowpark DataFrame. This data is then saved as a table in Snowflake\r\n\r\n```python\r\n# Read data and create Snowpark DataFrame\r\ndf_clean = pd.read_csv('CTA_Daily_Totals_by_Route.csv')\r\ndf_clean.columns = df_clean.columns.str.upper()\r\ninput_df = session.create_dataframe(df_clean)\r\n\r\n# Save DataFrame as a table in Snowflake\r\ninput_df.write.mode('overwrite').save_as_table('MODEL_SERVING_DB.FEATURE_STORE_MLDEMO.CHICAGO_BUS_RIDES')\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Machine Learning\r\n\r\n### Create and Register a Feature Store\r\nA Feature Store is created to manage and version our features. We then register an Entity which represents a real-world object that our features are associated with, in this case, a bus route.\r\n\r\n```python\r\n# Create a feature store\r\nfs = FeatureStore(\r\n    session=session,\r\n    database=\"MODEL_SERVING_DB\",\r\n    name=\"FEATURE_STORE_MLDEMO\",\r\n    default_warehouse=\"DEMO_BUILD_WH\",\r\n    creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\r\n)\r\n\r\n# Register an entity\r\nentity = Entity(\r\n    name=\"route\",\r\n    join_keys=[\"DATE\"],\r\n)\r\nfs.register_entity(entity)\r\n```\r\n\r\n### Distributed Feature Engineering\r\nWe perform feature engineering to create new features that will be useful for our model. This includes extracting the day of the week and month from the date, and calculating the previous day's ridership. These engineered features are then registered as a FeatureView.\r\n\r\n```python\r\n# Perform feature engineering\r\nfrom snowflake.snowpark.functions import col, to_timestamp, dayofweek, month, sum, lag\r\nfrom snowflake.snowpark import Window\r\n\r\ndf = session.read.table('MODEL_SERVING_DB.FEATURE_STORE_MLDEMO.CHICAGO_BUS_RIDES')\r\ndf = df.with_column('DATE', to_timestamp(col('DATE'), 'MM/DD/YYYY'))\r\ndf = df.with_column('DAY_OF_WEEK', dayofweek(col('DATE')))\r\ndf = df.with_column('MONTH', month(col('DATE')))\r\n\r\ntotal_riders = df.group_by('DATE','DAY_OF_WEEK','MONTH').agg(\r\n    F.listagg('DAYTYPE', is_distinct=True).alias('DAYTYPE'),\r\n    F.sum('RIDES').alias('TOTAL_RIDERS')\r\n).order_by('DATE')\r\n\r\nwindow_spec = Window.order_by('DATE')\r\ntotal_riders = total_riders.with_column('PREV_DAY_RIDERS', lag(col('TOTAL_RIDERS'), 1).over(window_spec))\r\n\r\n# Register the feature view\r\nagg_fv = FeatureView(\r\n    name=\"AggBusData\",\r\n    entities=[entity],\r\n    feature_df=total_riders,\r\n    timestamp_col=\"DATE\",\r\n)\r\nagg_fv = fs.register_feature_view(agg_fv, version=\"1\", overwrite=True)\r\n```\r\n\r\n### Model Training and Evaluation\r\nWith our features ready, we define a machine learning pipeline to preprocess the data and train an XGBoost Regressor model. The model is trained and evaluated in a distributed manner using Snowflake's compute resources.\r\n\r\nCreate and Train the Model\r\n\r\n```python\r\nfrom snowflake.ml.modeling.compose import ColumnTransformer\r\nfrom snowflake.ml.modeling.preprocessing import StandardScaler, OrdinalEncoder\r\nfrom snowflake.ml.modeling.pipeline import Pipeline\r\nfrom snowflake.ml.modeling.xgboost import XGBRegressor\r\nfrom snowflake.ml.modeling.model_selection import GridSearchCV\r\n\r\n# Define preprocessing steps\r\nnumeric_features = ['DAY_OF_WEEK','MONTH','PREV_DAY_RIDERS','MINIMUM_TEMPERATURE','MAXIMUM_TEMPERATURE','PRECIPITATION']\r\nnumeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])\r\n\r\ncategorical_cols = ['DAYTYPE']\r\ncategorical_transformer = Pipeline(steps=[('onehot', OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-99999))])\r\n\r\npreprocessor = ColumnTransformer(\r\n    transformers=[\r\n        ('num', numeric_transformer, numeric_features),\r\n        ('cat', categorical_transformer, categorical_cols)\r\n        ])\r\n\r\npipeline = Pipeline(steps=[('preprocessor', preprocessor),('model', XGBRegressor())])\r\n\r\n# Use GridSearchCV for hyperparameter tuning\r\nhyper_param = dict(model__max_depth=[2,4], model__learning_rate=[0.1,0.3])\r\nxg_model = GridSearchCV(\r\n    estimator=pipeline,\r\n    param_grid=hyper_param,\r\n    input_cols=numeric_features + categorical_cols,\r\n    label_cols=['TOTAL_RIDERS'],\r\n    output_cols=[\"TOTAL_RIDERS_FORECAST\"],\r\n)\r\n\r\n# Fit the model\r\nxg_model.fit(train)\r\n```\r\n\r\n### Model Lineage\r\nSnowflake’s UI provides a graphical representation of the lineage, making it easier to visualize and communicate your findings. To do so, we can go to the CHICAGO_BUS_RIDES_FORECAST table and click on lineage\r\n\r\n![4_lineage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_lineage.png)\r\n\r\n## Model Deployment\r\n\r\n### Evaluate the Model\r\nAfter training, we evaluate the model's performance on the test set.\r\n\r\n```python\r\nfrom snowflake.ml.modeling.metrics import mean_absolute_error\r\n\r\ntestpreds = xg_model.predict(test)\r\nprint('MSE:', mean_absolute_error(df=testpreds, y_true_col_names='TOTAL_RIDERS', y_pred_col_names='\"TOTAL_RIDERS_FORECAST\"'))\r\n```\r\n\r\n### Model Registration and Deployment\r\nFinally, the trained model is logged to the Snowflake Model Registry. This allows for versioning, management, and easy deployment of the model.\r\n\r\n```python\r\nreg_model = reg.log_model(\r\n    model_name=\"Forecasting_Bus_Ridership\",\r\n    version_name=\"v2\",    \r\n    model=xg_model,\r\n    target_platforms=[\"WAREHOUSE\"],\r\n    conda_dependencies=[\"scikit-learn\",\"xgboost\"],\r\n    sample_input_data=train,\r\n    comment=\"My awesome XGBoost model\"\r\n)\r\n\r\n```\r\n\r\nWe are able to see the model stored inside the Model tab of the database. \r\n\r\n![4_model_served](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_model_served.png)\r\n\r\nClicking on the model, we can then further drill down to understand the model such as the model lineage. \r\n\r\n![4_lineage_model](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_lineage_model.png)\r\n\r\n### Deploy to Snowpark Container Services (SPCS)\r\nThe registered model can be deployed as a service in SPCS for real-time inference.\r\n\r\n```python\r\nreg_model = reg.get_model(\"Forecasting_Bus_Ridership\").version(\"v2\")\r\n\r\nreg_model.create_service(service_name=\"ChicagoBusForecastv12\",\r\n                  service_compute_pool=\"mypool\",\r\n                  ingress_enabled=True)\r\n```\r\n\r\n### Seeing the Inference endpoint\r\nTo see our inference endpoint, we can go to our model and click on the Inference Services tab. Opening the details, we can futher view the logs of our inference service\r\n\r\n![4_inference_serving](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/4_inference_serving.png)\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Model Serving\r\n\r\nThe Snowflake Model Registry, when used with containers, allows you to package, manage, and deploy machine learning models with complex dependencies or custom runtime environments inside Snowflake. This is accomplished by using Snowpark Container Services (SPCS) which we have done above. Some parts of the architecture for [model serving](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container) are as described below.\r\n\r\n![5_architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/5_architecture.png)\r\n\r\n- Inference Server: This is the engine that runs the model to serve predictions. It can use multiple processes to maximize the node's resources and includes an admission control feature to manage incoming requests, preventing overloads. Currently, Snowflake offers a flexible Python-based server, with plans to introduce servers optimized for specific model types in the future.\r\n- Model-specific Python environment: To minimize the startup time for a model, Snowflake creates a container that packages the model with all its specific dependencies.\r\n- Service functions: These functions allow you to call the inference server from code running within a Snowflake warehouse. They are designed with the same signature as the original model and use the external function protocol to communicate with the server.\r\n- Ingress endpoint: An optional HTTP endpoint can be created, making the model accessible to applications outside of Snowflake over the public internet. This is achieved when we did the create service in the previous section. \r\n\r\nOur model is now deployed and ready to serve predictions. The service endpoint is secured and requires authentication. We will explore two common methods for accessing it: Key-Pair Authentication for service accounts and Programmatic Access Tokens (PAT) for user-centric access.\r\n\r\nFirst, you need the service's ingress URL. You can find it by running the following SQL command:\r\n```sql\r\nSHOW ENDPOINTS IN SERVICE MODEL_SERVING_DB.FEATURE_STORE_MLDEMO.CHICAGOBUSFORECASTV12\r\n```\r\n\r\nThe full prediction endpoint will be \r\n```\r\nhttps://\u003CYOUR_INGRESS_PATH\u003E/predict\r\n```\r\npredict here comes from the function exposed by the model. To know all the functions exposed in a model, you can use:\r\n\r\n```\r\nSHOW FUNCTIONS IN MODEL Forecasting_Bus_Ridership VERSION v2;\r\n```\r\n\r\n### Using Key-Pair Authentication\r\n\r\nThis method is ideal for applications and service accounts. It involves using a public/private key pair to generate a session token for authentication. We will set up our SnowSQL connection and have a named connection in your [connections.toml](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-session#connect-by-using-the-connections-toml-file). In this example our named connection is called **\"named_connection\"**\r\n\r\n```yaml\r\n[named_connection]\r\n#Can be used in SnowSql as #connect example\r\naccountname = XXX\r\nusername = YYY\r\nrole = myrole\r\n```\r\n\r\nWe will be passing in some dummy data and transforming the DataFrame into a JSON-like format before passing it to our predict URL endpoint. The full code can be found below.\r\n\r\n```python\r\ntest = [[6 ,7,\"A\",486139,734535,\"USW00014819\",22.8,34.4,3.6 ,'2019-07-13'],  \r\n[6 ,6,\"A\",444588,784734,\"USW00014819\",13.9,21.7,5.1 ,'2019-06-15'],\r\n[4 ,6,\"W\",780768,765409,\"USW00014819\",11.1,20.6,0.5 ,'2019-06-13']]\r\n\r\ndf = pd.DataFrame(test, columns=['DAY_OF_WEEK' ,'MONTH' ,'DAYTYPE' ,'TOTAL_RIDERS' ,'PREV_DAY_RIDERS' ,'NOAA_WEATHER_STATION_ID' ,'MINIMUM_TEMPERATURE' ,'MAXIMUM_TEMPERATURE' ,'PRECIPITATION' ,'DATE'])\r\n\r\ndata = {\"data\": np.column_stack([range(df.shape[0]), df.values]).tolist()}\r\n```\r\n\r\nOnce done, save the code inside a file called \"KP-app.py\" and do \"\r\n\r\n```shell\r\npython KP-app.py\r\n```\r\n\r\n\r\n\r\n```python\r\nimport json\r\nfrom pprint import pprint\r\nimport requests\r\nimport snowflake.connector\r\nfrom snowflake.ml.utils.connection_params import SnowflakeLoginOptions\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n# --- 1. Connection and Authentication ---\r\n# This part of your code is correct. It gets a valid token.\r\ndef initiate_snowflake_connection():\r\n    # IMPORTANT: Ensure \"named_connection\" is correctly defined in your\r\n    # ~/.snowflake/connections.toml file.\r\n    connection_parameters = SnowflakeLoginOptions(\"named_connection\")\r\n    connection_parameters[\"session_parameters\"] = {\"PYTHON_CONNECTOR_QUERY_RESULT_FORMAT\": \"json\"}\r\n    connection_parameters[\"insecure_mode\"] = True  # Disable SSL verification\r\n    snowflake_conn = snowflake.connector.connect(**connection_parameters)\r\n    return snowflake_conn\r\n\r\ndef get_headers(snowflake_conn):\r\n    token = snowflake_conn._rest._token_request('ISSUE')\r\n    headers = {'Authorization': f'Snowflake Token=\"{token[\"data\"][\"sessionToken\"]}\"'}\r\n    return headers\r\n\r\nprint(\"Connecting to Snowflake to get auth token...\")\r\nheaders = get_headers(initiate_snowflake_connection())\r\nprint(\"Token acquired successfully.\")\r\n\r\n# --- 2. Endpoint URL ---\r\n# This must be the public endpoint for your service.\r\nURL = 'https://\u003CYOUR_INGRESS_PATH\u003E/predict'\r\n\r\n# --- 3. Preparing our data ---\r\ndef prepare_data():\r\n    # Ensure 'test' is defined before this point\r\n    # Assuming 'test' is a Snowpark DataFrame object\r\n    test = [[6 ,7,\"A\",486139,734535,\"USW00014819\",22.8,34.4,3.6 ,'2019-07-13'],  \r\n[6 ,6,\"A\",444588,784734,\"USW00014819\",13.9,21.7,5.1 ,'2019-06-15'],\r\n[4 ,6,\"W\",780768,765409,\"USW00014819\",11.1,20.6,0.5 ,'2019-06-13']]\r\n\r\n    df = pd.DataFrame(test, columns=['DAY_OF_WEEK' ,'MONTH' ,'DAYTYPE' ,'TOTAL_RIDERS' ,'PREV_DAY_RIDERS' ,'NOAA_WEATHER_STATION_ID' ,'MINIMUM_TEMPERATURE' ,'MAXIMUM_TEMPERATURE' ,'PRECIPITATION' ,'DATE'])\r\n    print(df)\r\n\r\n    data = {\"data\": np.column_stack([range(df.shape[0]), df.values]).tolist()}     \r\n    print(data)\r\n    return data\r\n\r\nsample_df = prepare_data()\r\n\r\n\r\n# --- 4. Sending the Request ---\r\ndef send_request(data: dict, headers: dict):\r\n    \"\"\"Posts the data to the URL and handles the response.\"\"\"\r\n    print(\"nSending request to endpoint...\")\r\n    try:\r\n        output = requests.post(URL, json=sample_df, headers=headers)\r\n        \r\n        print(\"--- Response from Server ---\")\r\n        print(f\"Status Code: {output.status_code}\")\r\n        \r\n        # Try to print JSON if possible, otherwise print raw text\r\n        try:\r\n            response_json = output.json()\r\n            print(\"Response JSON:\")\r\n            pprint(response_json)\r\n        except json.JSONDecodeError:\r\n            print(\"Raw Response Text:\")\r\n            print(output.text)\r\n            \r\n        return output\r\n\r\n    except requests.exceptions.RequestException as e:\r\n        print(f\"An error occurred during the request: {e}\")\r\n        return None\r\n\r\n# --- Run the request ---\r\nsend_request(data=sample_df, headers=headers)\r\n\r\n```\r\n### Using Programmatic Access Token (PAT)\r\n\r\nPATs are excellent for interactive or user-driven scenarios. A user generates a token in the Snowsight UI and uses it to authenticate.\r\n\r\nSetup: \r\n\r\n1. Sign in to Snowsight.\r\n2. Click on your username in the top left corner, then select My Profile.\r\n3. In the dialog that appears, scroll down to the Programmatic Access Tokens section.\r\n4. Click + Generate new token.\r\n5. Fill in the token details:\r\n- Name: A descriptive name for your token (e.g., dbt_cloud_token).\r\n- Expires in: Choose an expiration period (e.g., 90 days). It is a security best practice to set an expiration date.\r\n- Role: You can restrict the token to a specific role. This is highly recommended to limit the token's permissions to only what is necessary.\r\n6. Click Generate.\r\n7. A dialog box will display your new token. This is the only time the token secret will be shown. Copy it immediately. \r\n\r\n![6_PAT](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/6_PAT.png)\r\n\r\nOnce done, save the code below inside a file called \"PAT-app.py\" and do \r\n\r\n\r\n```shell\r\npython PAT-app.py \r\n  --account \u003CACCOUNT_IDENTIFIER\u003E \r\n  --user \u003CUSERNAME\u003E\u003E \r\n  --role ACCOUNTADMIN \r\n  --pat \u003CPASS_IN_YOUR_PAT_TOKEN\u003E\r\n  --endpoint \u003CYOUR_INGRESS_PATH\u003E \r\n  --endpoint-path /predict\r\n```\r\n\r\n```python\r\nfrom datetime import timedelta\r\nimport argparse\r\nimport logging\r\nimport sys\r\nimport requests\r\nimport numpy as np\r\nfrom pprint import pprint\r\nimport json\r\nimport pandas as pd\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\ndef main():\r\n    args = _parse_args()\r\n    spcs_access_token = token_exchange_pat(args.pat, endpoint=args.endpoint,\r\n           role=args.role,\r\n           snowflake_account_url=args.snowflake_account_url, \r\n      snowflake_account=args.account)\r\n    spcs_url=f'https://{args.endpoint}{args.endpoint_path}'\r\n    connect_to_spcs(spcs_access_token, spcs_url)\r\n\r\ndef token_exchange_pat(pat, role, endpoint, snowflake_account_url, snowflake_account) -\u003E str:\r\n    scope_role = f'session:role:{role}' if role is not None else None\r\n    scope = f'{scope_role} {endpoint}' if scope_role is not None else endpoint\r\n\r\n    data = {\r\n        'grant_type': 'urn:ietf:params:oauth:grant-type:token-exchange',\r\n        'scope': scope,\r\n        'subject_token': pat,\r\n        'subject_token_type': 'programmatic_access_token'\r\n    }\r\n    logger.info(data)\r\n    url = f'https://{snowflake_account}.snowflakecomputing.com/oauth/token'\r\n    if snowflake_account_url:\r\n        url = f'{snowflake_account_url}/oauth/token'\r\n    response = requests.post(url, data=data)\r\n    logger.info(\"snowflake token : %s\" % response.text)\r\n    logger.info(\"snowflake token response code : %s\" % response.status_code)\r\n    assert 200 == response.status_code, \"unable to get snowflake token\"\r\n    return response.text\r\n\r\n\r\ndef prepare_data():\r\n    # Ensure 'test' is defined before this point\r\n    # Assuming 'test' is a Snowpark DataFrame object\r\n    test = [[6 ,7,\"A\",486139,734535,\"USW00014819\",22.8,34.4,3.6 ,'2019-07-13'],  \r\n[6 ,6,\"A\",444588,784734,\"USW00014819\",13.9,21.7,5.1 ,'2019-06-15'],\r\n[4 ,6,\"W\",780768,765409,\"USW00014819\",11.1,20.6,0.5 ,'2019-06-13']]\r\n\r\n    df = pd.DataFrame(test, columns=['DAY_OF_WEEK' ,'MONTH' ,'DAYTYPE' ,'TOTAL_RIDERS' ,'PREV_DAY_RIDERS' ,'NOAA_WEATHER_STATION_ID' ,'MINIMUM_TEMPERATURE' ,'MAXIMUM_TEMPERATURE' ,'PRECIPITATION' ,'DATE'])\r\n    print(df)\r\n\r\n    data = {\"data\": np.column_stack([range(df.shape[0]), df.values]).tolist()}     \r\n    print(data)\r\n    return data\r\n       \r\n\r\ndef connect_to_spcs(token, url):\r\n  # Create a request to the ingress endpoint with authz.\r\n  headers = {'Authorization': f'Snowflake Token=\"{token}\"'}\r\n  logger.info(url)\r\n  request_body = prepare_data()  # Convert the DataFrame to the right format\r\n  print (request_body)\r\n  response = requests.post(f'{url}',data=json.dumps(request_body), headers=headers)     #data=json.dumps(request_body)\r\n  #assert (response.status_code == 200), f\"Failed to get response from the service. Status code: {response.status_code}\"\r\n  #return response.content\r\n  logger.info(\"return code %s\" % response.status_code)\r\n  logger.info(response.text)\r\n  logger.info(response.content)\r\n  logger.debug(response.content)\r\n\r\ndef _parse_args():\r\n  logging.basicConfig(stream=sys.stdout, level=logging.INFO)\r\n  cli_parser = argparse.ArgumentParser()\r\n  cli_parser.add_argument('--account', required=True,\r\n              help='The account identifier (for example, \"myorganization-myaccount\" for '\r\n                '\"myorganization-myaccount.snowflakecomputing.com\").')\r\n  cli_parser.add_argument('--user', required=True, help='The user name.')\r\n  cli_parser.add_argument('--role',\r\n              help='The role we want to use to create and maintain a session for. If a role is not provided, '\r\n                'use the default role.')\r\n  cli_parser.add_argument('--endpoint', required=True,\r\n              help='The ingress endpoint of the service')\r\n  cli_parser.add_argument('--endpoint-path', default='/',\r\n              help='The url path for the ingress endpoint of the service')\r\n  cli_parser.add_argument('--snowflake_account_url', default=None,\r\n              help='The account url of the account for which we want to log in. Type of '\r\n                'https://myorganization-myaccount.snowflakecomputing.com')\r\n  cli_parser.add_argument('--pat',\r\n                            help='PAT for the user.')\r\n  args = cli_parser.parse_args()\r\n  return args\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n\r\n```\r\n\r\nIf all is well you should get a response like below\r\n\r\n![7_response](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowpark-container-services-model-serving-guide/7_response.png)\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion And Resources\r\n\r\nCongratulations! You have successfully built, deployed, and accessed a machine learning model using Snowpark Container Services. You've seen how [Snowflake ML](/en/product/use-cases/end-to-end-ml-workflows/) can manage the entire MLOps lifecycle within a single, unified platform, from data engineering to production deployment.\r\n\r\nThis powerful combination of Snowpark, the Model Registry, and SPCS enables data teams to build and scale AI/ML applications securely and efficiently.\r\n\r\n### What You Learned\r\n- Setting up a complete ML environment in Snowflake.\r\n- Training and versioning models with the Snowflake Model Registry.\r\n- Deploying models as scalable services using SPCS.\r\n- Authenticating and consuming model predictions from a secure endpoint.\r\n\r\n### Related Resources\r\n- [Snowflake ML homepage](/en/product/use-cases/end-to-end-ml-workflows/)\r\n- [More ML Quickstart](https://docs.snowflake.com/en/developer-guide/snowflake-ml/quickstart)\r\n- [Model Serving in SPCS](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/container)\r\n- [Source Code on GitHub](https://github.com/Snowflake-Labs/sfguide-getting-started-with-model-serving-in-spcs)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-7c94e27aae","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":{"layout":"SIMPLE","id":"container-f5837dfbd2",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-fc7c0eb9b2","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-06-26",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-ace10e43b7","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 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