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Organizations frequently allocate significant resources to model development, only to encounter restricted visibility once models enter production systems. Inference data is often fragmented across disparate logging mechanisms, while active model versions remain unindexed at the point of prediction. This systemic lack of transparency impedes rigorous root-cause analysis and escalates operational risk during model deployment cycles.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"http://www.snowflake.com/ml\"\u003ESnowflake ML’\u003C/a\u003Es inference infrastructure is designed to resolve this architectural disconnect. From initial registration within the Model Registry to execution on Snowpark Container Services (SPCS), the platform automatically captures prediction outputs and tracks model versions. Consequently, experimental results are directly quantifiable via SQL, contained within Snowflake’s unified governance and security framework.\u003C/p\u003E\r\n\u003Cp\u003EThis article examines post-deployment operations: automated data logging, deterministic request routing, continuous observability and controlled release strategies. To learn more about the serving architecture, see our previous post on Model Serving \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/scalable-model-serving-architecture/\"\u003EArchitecture\u003C/a\u003E and the companion publication on \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/scale-real-time-model-serving/\"\u003EScaling Real-Time Inference\u003C/a\u003E.\u003C/p\u003E\r\n\u003Ch3\u003EPrimary operational challenges in model serving in production\u003C/h3\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EStable endpoints across model updates:\u003C/b\u003E Promoting candidate model versions or executing experimental runs frequently necessitates endpoint URL reconfigurations, thereby introducing operational overhead. Maintaining a version-agnostic interface is crucial for ensuring client integration stability.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EComplete prediction audit trail built-in:\u003C/b\u003E Fragmented data logging impedes the correlation of input feature vectors with output predictions. A persistent, queryable historical record is essential for systematic debugging, auditing and compliance verification.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EModel quality monitoring beyond infrastructure health:\u003C/b\u003E Monitoring infrastructure availability alone is insufficient. Engineering teams require rigorous statistical visibility into covariate shift, distribution drift and model degradation by evaluating relative performance against ground-truth labels.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003ESnowflake systematically addresses these challenges through a four-stage operational lifecycle: \u003Cb\u003EAutomated Logging\u003C/b\u003E of prediction payloads via Inference Tables, \u003Cb\u003EIngestion\u003C/b\u003E of ground-truth outcomes, \u003Cb\u003EEvaluation\u003C/b\u003E of statistical drift and predictive fidelity via Model Monitoring, and \u003Cb\u003EGovernance\u003C/b\u003E regarding model promotion via Gateway routing. The subsequent sections elaborate on each phase.\u003C/p\u003E\r\n\u003Ch1\u003EAutomated prediction logging (Auto-Capture)\u003C/h1\u003E\r\n\u003Cp\u003EProduction inference pipelines frequently exhibit structural fragmentation: Client applications log input parameters to heterogeneous messaging systems or object stores, while downstream services log model responses to separate storage destinations. This architecture forces organizations to maintain custom correlation keys, stream consumers and complex reconciliation extract, transform, load (ETL) pipelines. Consequently, network timeouts and processing latencies routinely introduce data staleness or orphaned records.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EInference Tables\u003C/b\u003E remove this operational overhead. Upon enabling automated capture for a given model service, inference transactions are asynchronously written directly to the model’s dedicated inference table — consolidating inputs, outputs and execution metadata without requiring bespoke logging infrastructure.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-d4ab6f8590","language":"python","codeSnippet":"\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Emv.create_service(\r\n    service_name=\"my_service\",\r\n    service_compute_pool=\"my_compute_pool\",\r\n    autocapture=True\r\n)\u003C/code\u003E\u003C/pre\u003E","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_82625409":{"id":"blog-text-7c855aca37","text":"\u003Cp\u003EThe platform automatically records a comprehensive, structured record for each service invocation:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ETemporal attributes:\u003C/b\u003E High-precision execution time stamps.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETransaction payloads:\u003C/b\u003E Complete input feature vectors and corresponding prediction responses.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EExecution metadata:\u003C/b\u003E Model version identifiers, service endpoints and invocation path vectors (for example, \u003Ccode\u003E/predict\u003C/code\u003E).\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EContextual attributes:\u003C/b\u003E Gateway routing hops and user-defined correlation attributes facilitating downstream database joins.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ETo support auditability and data integrity, inference tables operate in an immutable, append-only mode. Capture operations execute asynchronously, so logging latency remains decoupled from the critical path of the primary prediction response.\u003C/p\u003E\r\n\u003Ch3\u003ELatency and throughput performance evaluation\u003C/h3\u003E\r\n\u003Cp\u003EWe conducted empirical evaluations to assess the performance impact of automated capture using an XGBoost classification model deployed on a single \u003Ccode\u003ECPU_X64_L\u003C/code\u003E instance, benchmarking execution metrics across varying levels of request concurrency.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ELatency impact:\u003C/b\u003E Activating automated capture introduced an end-to-end median latency increase of \u003Cb\u003E<10 ms p50\u003C/b\u003E (Figure 1).\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EThroughput variance:\u003C/b\u003E Overall transaction throughput varied by \u003Cb\u003E<1%\u003C/b\u003E across the concurrency benchmark spectrum (Figure 2).\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EA marginal increase in CPU utilization is expected on serving nodes when automated capture is active, reflecting the continuous streaming of payload data into persistent storage. This compute allocation is designed to support near real-time analytical capabilities while preserving latency and throughput bounds.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-e13f9a68fc","isLcpImage":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fc6c3a95-3f83-4b75-8517-3d119d291b21/figure-1.-operationalizing-model-serving--completing-the-feedback-loop-with-snowflake.png?preferwebp=true&quality=85","alt":"Figure 1. End-to-end latency comparative analysis (by concurrency level).","lazyEnabled":true,"height":"856","width":"1388","title":"Figure 1. End-to-end latency comparative analysis (by concurrency level).",":type":"snowflake-site/components/image"},"image_351836583":{"id":"image-c9d3f298b6","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1527de0f-3863-4613-9221-f5c86f16e0c4/figure-2.-operationalizing-model-serving--completing-the-feedback-loop-with-snowflake.png?preferwebp=true&quality=85","alt":"Figure 2. System throughput comparative analysis (by concurrency level).","lazyEnabled":true,"height":"856","width":"1420","title":"Figure 2. System throughput comparative analysis (by concurrency level).",":type":"snowflake-site/components/image"},"blog_text_127965915":{"id":"blog-text-cda98598f6","text":"\u003Ch3\u003EPayload governance and access control\u003C/h3\u003E\r\n\u003Cp\u003EAutomated capture stores transaction records under the model’s native role-based access control (RBAC) definitions, so only authorized principals are permitted to query stored payloads.\u003C/p\u003E\r\n\u003Cp\u003ETo enable asynchronous join operations with ground-truth records, pass an identifier, such as a transaction ID, as a custom attribute. Model owners retain full visibility into raw sensitive attributes transmitted during inference.\u003C/p\u003E\r\n\u003Ch1\u003EGround-truth label acquisition\u003C/h1\u003E\r\n\u003Cp\u003EDistributional drift metrics quantify variations in input features and model predictions. However, formal performance evaluation (for example, Accuracy, Precision, F1-score, RMSE) necessitates ground-truth labels, which rarely arrive synchronously at inference time. For example, financial fraud labels may materialize hours post-transaction, whereas customer attrition indicators may take several weeks to resolve.\u003C/p\u003E\r\n\u003Cp\u003ETo accommodate delayed labeling, an enterprise-managed ground-truth table can be linked via unique transaction identifiers captured during inference. As ground-truth records land in storage, monitoring tasks continuously ingest the new data to update analytical metrics.\u003C/p\u003E\r\n\u003Cp\u003EThis ground-truth table is integrated into a monitoring object via the \u003Ci\u003EGROUND_TRUTH\u003C/i\u003E and \u003Ci\u003EID_COLUMNS\u003C/i\u003E properties, as detailed in the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.google.com/document/d/1jv0ZJx5OI0UHEJzL3tR-JrFzVvcySgCSVr8yKvJQiq4/edit?tab=t.e5faatrpspqx#heading=h.bd3i6z7d9zh\"\u003EEvaluation section\u003C/a\u003E. This step is optional; if you don’t provide ground-truth labels, the monitor skips performance metrics.\u003C/p\u003E\r\n\u003Ch1\u003EQuantitative evaluation: model-aware observability\u003C/h1\u003E\r\n\u003Cp\u003ELogging predictions is only useful if you can measure whether the model is performing well, by using the right metrics specific to the ML task. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/model-observability\"\u003ESnowflake Model Monitoring\u003C/a\u003E dynamically adapts to the ML task defined in Model Registry — binary classification, regression or multiclass classification — and automatically computes the target drift and performance metrics tailored to the model type. Users do not need to specify which metrics to track; the monitoring engine automatically derives the appropriate statistical evaluation suite based on the schema of the registered model version, as shown in the table below, minimizing manual configuration requirements.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_555209288":{"id":"blog-text-ca48dfb28a","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EModel type\u003C/th\u003E\r\n\u003Cth\u003EDrift metrics\u003C/th\u003E\r\n\u003Cth\u003EPerformance metrics (when labels exist)\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EBinary classification\u003C/td\u003E\r\n\u003Ctd\u003EJensen–Shannon, PSI, Wasserstein†, Difference of Means†\u003C/td\u003E\r\n\u003Ctd\u003EAccuracy, Precision, Recall, F1, ROC-AUC‡\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ERegression\u003C/td\u003E\r\n\u003Ctd\u003EJensen–Shannon, PSI, Wasserstein, Difference of Means\u003C/td\u003E\r\n\u003Ctd\u003ERMSE, MAE, MAPE, MSE\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMulticlass classification\u003C/td\u003E\r\n\u003Ctd\u003EJensen–Shannon, PSI\u003C/td\u003E\r\n\u003Ctd\u003EAccuracy, Macro/Micro Precision & Recall\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"3\"\u003E\u003Ci\u003E† Wasserstein and Difference of Means need continuous scalars; for binary classification they run on prediction scores, not classes. They do not apply to multiclass.\u003C/i\u003E\u003Cbr\u003E\r\n\u003Ci\u003E‡ F1 and ROC-AUC assume a binary positive/negative framework; they do not natively apply to multiclass.\u003C/i\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1820020183":{"id":"blog-text-e73df1441d","text":"\u003Ch3\u003EArchitectural mechanics of model observability\u003C/h3\u003E\r\n\u003Cp\u003ESnowflake optimizes monitoring compute efficiency by maintaining preaggregated metric tables covering feature distributions, predictive accuracy and subpopulation segments. These structures utilize probabilistic data structures to enable scalable drift computation, preventing the need to rescan raw transaction logs during evaluation cycles.\u003C/p\u003E\r\n\u003Cp\u003ETo establish observability for a deployed model version, construct a source relational view over the raw inference table — flattening nested payload fields and joining ground-truth labels where available — and instantiate a model monitor object.\u003C/p\u003E\r\n\u003Cp\u003EYou can use Snowflake CoCo to generate both the source view and the DDL command for creating the model monitor:\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003E"The fraud detection model FRAUD_DETECTION_MODEL version V1 operates with automated capture active. Predictions include a PRED_CLASS column. A corresponding ground-truth table, FRAUD_GROUND_TRUTH, records late-arriving labels joined on a unique transaction ID stored as a custom attribute. Configure a source view and model monitor with an hourly refresh schedule over a two-day aggregation window."\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003EThe resulting command is as follows. First, to create the source view, the monitor expects a flattened schema. The source view performs the flattening step.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_848162056":{"id":"code-snippet-3015aa9d5e","language":"python","codeSnippet":"# Create the source view: generates a flattened view from the inference table\r\n# (flattens predictions, LEFT JOINs ground truth on your captured ID)\r\n\r\ninf_df = session.sql(\"\"\"\r\n    SELECT\r\n        RECORD_ATTRIBUTES['snow.model_serving.request.extra_columns.TRANSACTION_ID']::VARCHAR AS TRANSACTION_ID,\r\n        RECORD_ATTRIBUTES['snow.model_serving.response.data.PRED_CLASS']::NUMBER              AS PRED_CLASS,\r\n        TIMESTAMP\r\n    FROM TABLE(INFERENCE_TABLE('FRAUD_DETECTION_MODEL', MODEL_VERSION =\u003E 'V1'))\r\n    WHERE RECORD_ATTRIBUTES['snow.model_serving.function.name']::VARCHAR = 'predict'\r\n\"\"\")\r\n\r\ngt_df = session.table(\"FRAUD_GROUND_TRUTH\")\r\n\r\nsource_view = (\r\n    inf_df.join(gt_df, inf_df[\"TRANSACTION_ID\"] == gt_df[\"TRANSACTION_ID\"], \"left\")\r\n          .select(\r\n              inf_df[\"TRANSACTION_ID\"],\r\n              inf_df[\"PRED_CLASS\"],\r\n              inf_df[\"TIMESTAMP\"],\r\n              gt_df[\"LABEL_CLASS\"],\r\n          )\r\n)\r\n\r\nsource_view.create_or_replace_view(\"FRAUD_MONITOR_SOURCE\")","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_696431123":{"id":"blog-text-678f4e65d3","text":"\u003Cp\u003ENext, create the model monitor with the FRAUD_MONITOR_SOURCE source view. The REFRESH_INTERVAL controls how often new predictions are ingested. AGGREGATION_WINDOW controls how far back the drift and performance metrics look. Setting a two-day window here means each hourly refresh recalculates metrics over a rolling 48-hour slice of predictions. For our fraud detection example, this means that the refresh is recent enough to catch degradation quickly, yet wide enough to smooth out low-traffic periods.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_596869637":{"id":"code-snippet-51b56bc728","language":"python","codeSnippet":"# Create model monitor with the source view \r\n\r\nfrom snowflake.ml.monitoring.entities.model_monitor_config import ModelMonitorConfig, ModelMonitorSourceConfig\r\n\r\nsource_config = ModelMonitorSourceConfig(\r\n    source=\"FRAUD_MONITOR_SOURCE\",\r\n    timestamp_column=\"TIMESTAMP\",\r\n    id_columns=[\"TRANSACTION_ID\"],\r\n    prediction_class_columns=[\"PRED_CLASS\"],\r\n    actual_class_columns=[\"LABEL_CLASS\"],\r\n    baseline=\"FRAUD_DETECTION_BASELINE\",\r\n)\r\nmonitor_config = ModelMonitorConfig(\r\n    model_version=reg.get_model(\"FRAUD_DETECTION_MODEL\").version(\"V1\"),\r\n    model_function_name=\"predict\",\r\n    background_compute_warehouse_name=\"MY_WAREHOUSE\",\r\n    refresh_interval=\"1 hour\",\r\n    aggregation_window=\"2 days\",\r\n)\r\nmonitor = reg.add_monitor(\"MY_VERSION_MONITOR\", source_config, monitor_config)","multiLine":true,":type":"snowflake-site/components/code-snippet"},"image_1348868002":{"id":"image-f65b736112","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--7c653871-24b0-4f41-892b-13ccec6be2d1/figure-3.-operationalizing-model-serving--completing-the-feedback-loop-with-snowflake.png?preferwebp=true&quality=85","alt":"Figure 3. Structural architecture of model monitoring (Inference Table → Aggregates → Metrics).","lazyEnabled":true,"height":"920","width":"2048",":type":"snowflake-site/components/image"},"blog_text_341247711":{"id":"blog-text-3d24726e0b","text":"\u003Ch1\u003EModel promotion and controlled deployment strategies: traffic splitting, shadowing\u003C/h1\u003E\r\n\u003Cp\u003ENow that you have logged the predictions and monitored them, you need a way to test and validate new candidate model versions on real traffic before deploying them.\u003C/p\u003E\r\n\u003Cp\u003EA \u003Cb\u003EGateway\u003C/b\u003E solves this problem by serving as an abstraction layer fronting one or more backing model services. Client applications interface with a single, persistent endpoint URL while the gateway enforces configured traffic allocation policies. Consequently, model updates no longer require coordinated client-side changes.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_339584826":{"id":"code-snippet-ebb85c0b95","language":"python","codeSnippet":"session.sql(\"\"\"\r\n    CREATE GATEWAY FRAUD_DETECTION_XGBOOST_GATEWAY FROM SPECIFICATION $$\r\n    spec:\r\n      type: traffic_split\r\n      split_type: custom\r\n      targets:\r\n      - type: endpoint\r\n        value: mydb.myschema.FRAUD_DETECTION_SERVICE_PROD!inference\r\n        weight: 90\r\n      - type: endpoint\r\n        value: mydb.myschema.FRAUD_DETECTION_CANARY_SERVICE!inference\r\n        weight: 10\r\n    $$\r\n\"\"\").collect()","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1949455521":{"id":"blog-text-28218dad3c","text":"\u003Cp\u003E\u003Cb\u003ETraffic splitting:\u003C/b\u003E Routes a defined proportion of live requests to a candidate model (for example, 10%) while the baseline version processes the remaining volume. Both deployments handle live requests and write transaction records to their respective inference tables. This allows users to compare on live traffic distribution and scale up traffic allocation iteratively (for example, 10/90 → 30/70 → 100/0) as empirical confidence increases.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EShadow deployments:\u003C/b\u003E Shadow deployments (in public preview) are used for evaluating candidate performance against production traffic distributions without impacting client-facing applications. It asynchronously duplicates incoming production traffic and replays the requests against a candidate service. Candidate predictions are logged exclusively for offline comparison rather than returned to the client, so production users see only the primary service’s response.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1247647982":{"id":"image-e91c3ec523","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9de2f4cc-4caf-4b87-9414-73604eda81ec/figure-4.-operationalizing-model-serving--completing-the-feedback-loop-with-snowflake.png?preferwebp=true&quality=85","alt":"Figure 4.a: Gateway topology and traffic splitting configuration.","lazyEnabled":true,"height":"934","width":"2048","title":"Figure 4.a: Gateway topology and traffic splitting configuration.",":type":"snowflake-site/components/image"},"image_1747241929":{"id":"image-e1c8b2c456","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f7e6e487-18d2-4974-8294-913fd4830f13/figure-5.-operationalizing-model-serving--completing-the-feedback-loop-with-snowflake.png?preferwebp=true&quality=85","alt":"Figure 4.b: Infrastructure resource metrics comparison across production and canary services.","lazyEnabled":true,"height":"1044","width":"2048","title":"Figure 4.b: Infrastructure resource metrics comparison across production and canary services.",":type":"snowflake-site/components/image"},"blog_text_1667942236":{"id":"blog-text-04870bdf24","text":"\u003Ch1\u003EEvaluating A/B testing results\u003C/h1\u003E\r\n\u003Cp\u003EA \u003Cb\u003EVersion Monitor\u003C/b\u003E tracks a single deployed model version. Conversely, a \u003Cb\u003EGateway Monitor\u003C/b\u003E dynamically inspects all active service endpoints configured behind a gateway, applying uniform monitoring principles to facilitate \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/inference/gateway-monitor-and-ab-testing\"\u003Elive comparative A/B testing\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003EA Gateway Monitor can be created using the Snowflake CoCo prompt:\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003E"Construct a Gateway Monitor named FRAUD_AB_MONITOR for FRAUD_DETECTION_MODEL operating behind FRAUD_DETECTION_XGBOOST_GATEWAY. Track drift metrics across all attached services with an hourly refresh schedule over a one-hour aggregation window using warehouse MY_WAREHOUSE. Integrate FRAUD_GROUND_TRUTH joined on TRANSACTION_ID to evaluate classification accuracy over a 24-hour window."\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003ELeading to the following DDL:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1917946873":{"id":"code-snippet-c7015623f8","language":"python","codeSnippet":"session.sql(\"\"\"\r\n    CREATE MODEL MONITOR MY_GATEWAY_MONITOR\r\n    WITH\r\n      MODEL      = 'FRAUD_DETECTION_MODEL'\r\n      GATEWAY    = 'FRAUD_DETECTION_XGBOOST_GATEWAY'\r\n      FUNCTION   = 'predict'\r\n      WAREHOUSE  = MY_WAREHOUSE\r\n      REFRESH_INTERVAL   = '1 hour'\r\n      AGGREGATION_WINDOW = '24 hours'\r\n      GROUND_TRUTH = FRAUD_GROUND_TRUTH\r\n      ID_COLUMNS = ('TRANSACTION_ID')\r\n      ACTUAL_CLASS_COLUMNS    = (LABEL_CLASS)\r\n      PREDICTION_CLASS_COLUMNS = (PRED_CLASS)\r\n\"\"\").collect()","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1793998928":{"id":"blog-text-ee890d3beb","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003EThe instantiated monitor calculates drift indices and system metrics prior to the arrival of ground-truth labels. As records accumulate within the ground-truth relation, performance metrics automatically re-evaluate during subsequent refresh cycles. In the absence of ground-truth data, feature and prediction drift calculations continue to function independently.\u003C/p\u003E\r\n\u003Cp\u003EGateway Monitors support aggregation windows as short as one hour, enabling rapid evaluation during live experiments. In low-volume candidate traffic splits (for example, 10%), statistical confidence intervals around candidate performance metrics remain relatively wide. Promotion decisions require demonstrating not only higher point-estimate performance, but also that these intervals have sufficiently narrowed to confirm significance.\u003C/p\u003E\r\n\u003Cp\u003EDuring active A/B testing, each gateway target independently aggregates joined outcome labels, delivering statistically rigorous comparative assessments to guide promotion decisions. Predictive performance gains must be evaluated alongside system-level efficiency. A candidate model showing superior predictive accuracy may nevertheless be unsuitable for promotion if it introduces excessive latency, elevated memory overhead or instability under load. Gateway Monitoring consolidates transaction rates, error frequencies, latency percentiles, CPU usage and memory consumption into unified dashboards alongside data drift and accuracy metrics. Promotion criteria should dictate that model quality gains be verified concurrently with infrastructure stability.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","code_snippet","blog_text_82625409","image","image_351836583","blog_text_127965915","blog_text_555209288","blog_text_1820020183","code_snippet_848162056","blog_text_696431123","code_snippet_596869637","image_1348868002","blog_text_341247711","code_snippet_339584826","blog_text_1949455521","image_1247647982","image_1747241929","blog_text_1667942236","code_snippet_1917946873","blog_text_1793998928"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-cba70b68b3",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"author_chip":{"id":"author-chip-3b732abe25","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-307e026235","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ac437238-04bb-4150-a738-963f93a332c5/sumit-sardana.png?preferwebp=true&quality=85","lazyEnabled":true,"height":"400","width":"400",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-36a31e0594","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/sumit-sardana/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Sumit Sardana"},"authorTitle":"Senior Software 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