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border-top","layout":"SIMPLE","id":"container-6e8b91b43d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text":{"id":"text-8a8b23942e","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EMachine Learning Topics:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_1480319870":{"id":"text-9225108962","additionalClasses":"related-topics","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/automl/\"\u003EAutoML\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/deep-learning/\"\u003EDeep Learning\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/ensemble-learning/\"\u003EEnsemble Learning\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/feature-engineering/\"\u003EFeature Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/frameworks/\"\u003EML Frameworks\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/inference/\"\u003EML Inference\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/models/\"\u003EML Models\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/deep-learning/neural-network/\"\u003ENeural Networks\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/reinforcement-learning/\"\u003EReinforcement Learning\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text","text_1480319870"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_663228916":{"id":"flexible-column-container-ceb3810b5d","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout__0":{"id":"text-4447640d8c","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EML MODEL EVALUATION DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EModel evaluation is the systematic process of measuring how well a machine learning model performs during development, validation and testing before it’s deployed.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-57b888f295","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/models/\"\u003EMachine learning models\u003C/a\u003E are powerful because they can identify complex relationships in data — but as Anupam Datta, Principal Research Scientist at Snowflake, warns, they can also “latch onto spurious correlations, change over time or replicate human biases.” Models learn from examples, and those examples are imperfect: they may contain patterns that are coincidental, conditions that don’t last in production or traces of poor decisions.\u003C/p\u003E\n\u003Cp\u003EModel evaluation addresses those risks by testing the assumptions behind the model’s performance. By combining validation results with task-specific metrics and error analysis, teams can understand how the model performs, where it fails and whether its behavior is suitable for the tasks it will be applied to in production.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ml-model-evaluation":{"id":"title-v2-99fc1ee9a2","additionalClasses":"anchor-title anchor-title--what-is-ml-model-evaluation","type":"heading2","lines":["What is ML model evaluation?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ml-model-evaluation_0":{"id":"text-e8064e1f43","text":"\u003Cp\u003EML model evaluation is the systematic assessment of model performance during development, validation and testing. Depending on the problem being solved, teams might examine accuracy, precision, recall, F1-score, AUC-ROC, mean squared error or ranking quality measures. Together, these metrics help estimate how a model is likely to perform when exposed to data it hasn’t seen before.\u003C/p\u003E\n\u003Cp\u003EEvaluation is often discussed alongside \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/model-monitoring/\"\u003Emodel monitoring\u003C/a\u003E because both disciplines are concerned with model quality. The difference is timing. Evaluation occurs before \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/mlops/model-deployment/\"\u003Edeployment\u003C/a\u003E and asks whether a model is ready to be used. Monitoring occurs after deployment and tracks whether that assessment remains valid as data, users and business conditions change. In other words, evaluation establishes the initial evidence for a model’s quality, while monitoring tests that evidence against real-world behavior.\u003C/p\u003E\n\u003Cp\u003EThe evaluation process typically begins with a validation data set that wasn’t used during training. By comparing predictions against known outcomes, teams can estimate how well a model is likely to generalize to new data. Additional techniques such as cross-validation, temporal validation and segment-level analysis provide a more complete picture of model quality.\u003C/p\u003E\n\u003Cp\u003EBeyond numerical metrics, evaluation often includes error analysis: a closer review of which predictions were wrong, where those errors occurred and whether they followed a pattern. By locating those failures, teams can decide whether the model is genuinely suitable for deployment or only appears strong at the summary level.\u003C/p\u003E\n\u003Cp\u003EBecause no metric fully captures model quality, evaluation requires skilled decision-making. Teams use evaluation to weigh competing priorities, assess the consequences of different types of errors and determine whether model behavior aligns with operational requirements.\u003C/p\u003E\n\u003Cp\u003EIn practice, this means deciding which trade-offs are acceptable. By combining metrics, validation techniques and error analysis, evaluation provides the evidence needed to make those decisions before deployment.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ml-model-evaluation-matters":{"id":"title-v2-252f86a93c","additionalClasses":"anchor-title anchor-title--why-ml-model-evaluation-matters","type":"heading2","lines":["Why ML model evaluation matters"],":type":"snowflake-site/components/title-v2"},"text_why-ml-model-evaluation-matters_0":{"id":"text-86ba66dd5b","text":"\u003Cp\u003EStrong metrics can create a false sense of confidence when they reduce model quality to a single number. A model may score well for the wrong reasons, perform unevenly across segments, depend on unstable features or make errors that are acceptable in one context and costly in another.\u003C/p\u003E\n\u003Cp\u003ETo determine whether a model is truly ready for deployment, evaluation has to examine both performance and behavior. Teams need to understand what each metric captures, which error types it obscures, how performance varies across validation slices and whether the selected threshold produces acceptable tradeoffs for the decision the model will support.\u003C/p\u003E\n\u003Cp\u003EEvaluation also creates a common basis for review. Data scientists, business leaders, risk teams and regulators often bring different definitions of acceptable performance. Metric portfolios, validation results, error analysis and documented acceptance criteria help those groups assess the same model from different perspectives before deployment.\u003C/p\u003E\n\u003Cp\u003EIncreasingly, evaluation evidence serves governance purposes as well. Across regulated industries and high-impact AI applications, organizations are expected to demonstrate that model behavior has been tested, understood and approved before deployment. Evaluation supplies the documentation and repeatable methodology needed to support those requirements.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-ml-model-evaluation-matters_0":{"id":"text-5e24a49531","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EStrong aggregate metrics can hide significant weaknesses. Before selecting a model, teams should examine multiple metrics, review error patterns and confirm that performance aligns with the business outcome that matters most.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_why-models-need-metric-portfolios":{"id":"title-v2-274bc93133","additionalClasses":"anchor-title anchor-title--why-models-need-metric-portfolios","type":"heading2","lines":["Why models need metric portfolios"],":type":"snowflake-site/components/title-v2"},"text_why-models-need-metric-portfolios_0":{"id":"text-c09100d79a","text":"\u003Cp\u003EEvery metric highlights one aspect of model behavior while obscuring another: Accuracy measures overall correctness but can be misleading when classes are imbalanced. Precision focuses on false positives, while recall focuses on false negatives. Ranking metrics emphasize ordering rather than prediction accuracy. Even metrics that appear closely related can reward different behaviors.\u003C/p\u003E\n\u003Cp\u003EAs a result, model evaluation can’t rely on a single metric. Teams typically assemble metric portfolios that reflect the operational requirements of the use case. The goal is to build a set of measurements that captures the dimensions of performance most relevant to the decision the model will support.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_key-ml-model-evaluation-metrics":{"id":"title-v2-4b0e25ddeb","additionalClasses":"anchor-title anchor-title--key-ml-model-evaluation-metrics","type":"heading2","lines":["Key ML model evaluation metrics"],":type":"snowflake-site/components/title-v2"},"text_key-ml-model-evaluation-metrics_0":{"id":"text-a7a283b4dd","text":"\u003Cp\u003EEvaluation starts with the question the model is meant to answer. For example, a binary classifier, a regression model and a ranking model don’t fail in the same way, so they shouldn’t be judged by the same metric.\u003C/p\u003E\n\u003Ch3\u003EClassification metrics\u003C/h3\u003E\n\u003Cp\u003EClassification models assign records to categories. Common classification metrics include:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EAccuracy:\u003C/strong\u003E Measures the share of correct predictions across all predictions. Accuracy works best when classes are balanced and errors have roughly similar consequences.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrecision:\u003C/strong\u003E Focuses on the reliability of positive predictions. A high-precision model produces fewer false alarms, which is important when incorrect flags create extra work or friction, such as sending legitimate customers to manual review.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERecall:\u003C/strong\u003E Measures how many actual positives the model identified. Recall is important to measure when overlooked events carry significant risk, such as undetected fraud or missed high-risk activity.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EF1-score:\u003C/strong\u003E Combines precision and recall into a single metric, which can help when teams need to balance false positives and false negatives.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAUC-ROC:\u003C/strong\u003E Shows how well a model separates classes across different classification thresholds.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConfusion matrix:\u003C/strong\u003E Breaks predictions into true positives, false positives, true negatives and false negatives, making the error pattern visible instead of hiding it behind one aggregate score.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003ERegression metrics\u003C/h3\u003E\n\u003Cp\u003ERegression models predict continuous values, such as revenue, demand, risk scores, delivery time or customer lifetime value. These models are usually judged by how far predictions are from observed values.\u003C/p\u003E\n\u003Cp\u003ECommon regression metrics include:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EMean absolute error (MAE):\u003C/strong\u003E Measures the average absolute difference between predicted and actual values, which makes errors easy to interpret in the original unit.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMean squared error (MSE):\u003C/strong\u003E Squares errors before averaging them, which penalizes larger mistakes more heavily.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERoot mean squared error (RMSE):\u003C/strong\u003E Takes the square root of MSE, returning the metric to the original unit while still emphasizing larger errors.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ER-squared:\u003C/strong\u003E Estimates how much variance in the target the model explains.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMean absolute percentage error (MAPE):\u003C/strong\u003E Expresses error as a percentage, which can be helpful for business audiences but can behave poorly when actual values are close to zero.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EMetric choice should reflect the decision the model supports. For example, a supply chain team may care more about large forecast misses than small ones, which makes RMSE useful. A finance team may prefer MAE because it can be explained directly in dollars. A demand model for low-volume items may need something other than MAPE because percentage error can become unstable when actual demand is very small.\u003C/p\u003E\n\u003Ch3\u003ERanking metrics\u003C/h3\u003E\n\u003Cp\u003ERanking models order results rather than assign a single category or numeric value. Search, recommendation, feed ranking and retrieval systems often need metrics that account for the position of relevant results.\u003C/p\u003E\n\u003Cp\u003ECommon ranking metrics include:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ENormalized discounted cumulative gain (NDCG):\u003C/strong\u003E Measures ranking quality while giving more weight to relevant items near the top of the list.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMean average precision (MAP):\u003C/strong\u003E Evaluates how well relevant items are ranked across queries or users.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMean reciprocal rank (MRR):\u003C/strong\u003E Focuses on where the first relevant result appears.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ERanking metrics are especially important when the first few results carry most of the user impact. In a search experience, moving the right answer from position eight to position two may matter far more than improving the average relevance score across the full result list.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_key-ml-model-evaluation-metrics_0":{"id":"text-3f709244be","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ERelying on aggregate accuracy creates an imbalanced classification model. For example, a fraud model can look “97% accurate” while missing most of the rare fraudulent transactions, so teams should pair accuracy with precision, recall, confusion matrices and segment-level checks before and after deployment.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_model-quality-and-generalization":{"id":"title-v2-bf20529478","additionalClasses":"anchor-title anchor-title--model-quality-and-generalization","type":"heading2","lines":["Model quality and generalization"],":type":"snowflake-site/components/title-v2"},"text_model-quality-and-generalization_0":{"id":"text-d9fd0ca6ac","text":"\u003Cp\u003EEven a carefully selected metric portfolio can’t answer every evaluation question. Strong validation results are only meaningful if the model has learned patterns that extend beyond the data used for training. Generalization describes that ability to perform reliably on new, unseen data.\u003C/p\u003E\n\u003Ch3\u003EOverfitting\u003C/h3\u003E\n\u003Cp\u003EOverfitting happens when a model learns the training data too closely, including noise, anomalies or accidental correlations that don’t hold in new data. The model may show excellent training performance and weaker validation or test performance because it has memorized examples rather than learned durable patterns.\u003C/p\u003E\n\u003Cp\u003ECommon symptoms include a widening gap between training and validation metrics, increasingly complex decision boundaries or strong performance that fails to transfer across time periods, regions or customer segments. Remedies may include regularization, dropout, early stopping, simpler model architecture, more representative training data or better feature selection.\u003C/p\u003E\n\u003Ch3\u003EUnderfitting\u003C/h3\u003E\n\u003Cp\u003EUnderfitting happens when a model is too simple, too constrained or too poorly specified to capture the underlying pattern. The model performs poorly on both training and validation data because it hasn’t learned enough from the signal available.\u003C/p\u003E\n\u003Cp\u003ETeams may address underfitting by adding relevant features, reducing excessive regularization, increasing model complexity, improving feature transformations or selecting a model class better suited to the problem. The fix isn’t always to “use a bigger model” but rather to identify whether the current model has enough useful signal, capacity and training time to represent the problem.\u003C/p\u003E\n\u003Ch3\u003EBias-variance trade-off\u003C/h3\u003E\n\u003Cp\u003EThe bias-variance trade-off describes the tension between models that are too simple to capture the signal and models that are so flexible they become sensitive to noise. High-bias models tend to underfit. High-variance models tend to overfit.\u003C/p\u003E\n\u003Cp\u003EEvaluation helps teams locate a workable middle ground. A model doesn’t need to achieve perfect performance on the training data. It needs to perform reliably on unseen data, remain stable across relevant segments and improve the business decision it supports.\u003C/p\u003E\n\u003Ch3\u003EValidation strategies\u003C/h3\u003E\n\u003Cp\u003EValidation design shapes what evaluation can reveal. The same model can produce different performance estimates depending on how training and validation data are partitioned. An effective validation strategy mirrors the conditions under which the model will ultimately operate, providing a more reliable estimate of future performance.\u003C/p\u003E\n\u003Cp\u003EDifferent validation techniques address different risks. Random holdout validation may work for many classification problems, but it can produce misleading results when observations are time-dependent or highly imbalanced. Cross-validation improves estimate stability across multiple splits, while temporal validation preserves chronological order for forecasting and time-series problems.\u003C/p\u003E\n\u003Cp\u003ELearning curves can also help diagnose generalization issues. If training and validation performance are both poor, the model may be underfitting. If training performance is strong and validation performance is weak, the model may be overfitting. If validation performance improves with more data, the team may need broader or more representative training data rather than a different algorithm.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_evaluating-modern-llm-systems":{"id":"title-v2-591d7d4e4b","additionalClasses":"anchor-title anchor-title--evaluating-modern-llm-systems","type":"heading2","lines":["Evaluating modern LLM systems"],":type":"snowflake-site/components/title-v2"},"text_evaluating-modern-llm-systems_0":{"id":"text-ec799da90a","text":"\u003Cp\u003ETraditional model validation assumes that the team can compare predictions with a known target. This works well for many classification, regression and ranking problems, but \u003Ca href=\"https://www.snowflake.com/en/fundamentals/large-language-model/\"\u003ELLMs\u003C/a\u003E introduce a different evaluation problem: the output is open-ended, context-dependent and often judged by usefulness, correctness, safety, tone or grounding rather than exact match.\u003C/p\u003E\n\u003Ch3\u003ETraditional validation techniques\u003C/h3\u003E\n\u003Cp\u003EFor conventional ML models, validation techniques include holdout validation, k-fold cross-validation, stratified k-fold, leave-one-out validation and temporal splits. The goal is to estimate how the model will perform on data it hasn’t seen, while matching the way the model will operate in production.\u003C/p\u003E\n\u003Cp\u003EFor example, a demand forecast may use temporal splits to avoid training on future information, while a healthcare risk model may require segment-level validation to make sure aggregate performance doesn’t hide poor performance for a smaller population.\u003C/p\u003E\n\u003Ch3\u003EWhy LLM evaluation is different\u003C/h3\u003E\n\u003Cp\u003ELLM outputs don’t have ground truth in the way a classification label or regression target does. They may pull from unverified sources, or they may be complete by surface measures while omitting something materially important. In these cases, there’s no single metric that captures whether an output is actually useful for the task.\u003C/p\u003E\n\u003Cp\u003ETeams may still use quantitative metrics, but they often need human review, preference scoring, automated evaluators, safety checks and source-grounding tests to understand whether outputs are acceptable for the use case.\u003C/p\u003E\n\u003Ch3\u003ELLM-specific metrics and assessments\u003C/h3\u003E\n\u003Cp\u003ECommon LLM evaluation methods include \u003Ca href=\"https://huggingface.co/docs/transformers/perplexity\" target=\"_blank\"\u003Eperplexity\u003C/a\u003E, BLEU, ROUGE, BERTScore, human preference scoring and task-specific rubrics. Each has limits. BLEU and ROUGE can help compare generated text with reference text, but they don’t prove factual correctness. Human preference scoring can capture qualitative judgment, but it requires careful reviewer guidance and consistency checks.\u003C/p\u003E\n\u003Cp\u003EFor \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERAG systems\u003C/a\u003E, evaluation usually needs to inspect both retrieval and generation. Retrieval precision measures whether the system found relevant context. Answer faithfulness checks whether the generated response is supported by that context. Context relevance evaluates whether the retrieved material actually helps answer the question. Hallucination detection and factual grounding assessment matter because a confident answer can still be unsupported, outdated or inconsistent with the source record.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ml-model-evaluation-and-monitoring-on-snowflake":{"id":"title-v2-d92fca26e8","additionalClasses":"anchor-title anchor-title--ml-model-evaluation-and-monitoring-on-snowflake","type":"heading2","lines":["ML model evaluation and monitoring on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_ml-model-evaluation-and-monitoring-on-snowflake_0":{"id":"text-1f9ae78204","text":"\u003Cp\u003EML model evaluation depends on reproducibility. Training data, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/feature-engineering/\"\u003Efeature engineering\u003C/a\u003E, model versions, evaluation metrics and experimental results all need to remain connected so teams can compare models, reproduce results and justify deployment decisions. When those artifacts live across disconnected systems, evaluation is more difficult to reproduce and govern.\u003C/p\u003E\n\u003Cp\u003ESnowflake provides capabilities for model development, model management, monitoring and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003Egovernance\u003C/a\u003E alongside the data used for training. Teams can build, evaluate and manage many ML workflows within the Snowflake platform, reducing the need to move data between environments.\u003C/p\u003E\n\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E stores and manages model versions, model metrics and model metadata, and it supports model lifecycle management, model access control and monitoring through \u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EFor evaluation workflows, teams can use Snowflake Experiments to record training results and compare models before production selection. Snowflake ML Lineage helps teams trace ML artifacts from source data to features, data sets and models, which supports reproducibility, compliance and debugging across the ML lifecycle. Snowflake records lineage for models when they’re logged to the Model Registry, and training a model with Snowpark ML can automatically generate lineage records when the model is trained from a Snowpark DataFrame. Snowpark ML APIs allow teams to build evaluation workflows in Python while working with Snowflake data.\u003C/p\u003E\n\u003Cp\u003EThe advantage is that model metrics, prediction logs, lineage, monitoring jobs and governance controls sit closer to data and production workflows.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-confidence-before-deployment":{"id":"title-v2-e51cbe80af","additionalClasses":"anchor-title anchor-title--build-confidence-before-deployment","type":"heading2","lines":["Build confidence before deployment"],":type":"snowflake-site/components/title-v2"},"text_build-confidence-before-deployment_0":{"id":"text-07c00315db","text":"\u003Cp\u003EModel evaluation provides the evidence that a model is ready for deployment. Metrics, validation strategies and error analysis each contribute a different perspective on model behavior, helping teams move beyond a single performance score to understand how the model is likely to perform in production.\u003C/p\u003E\n\u003Cp\u003ENo evaluation process can eliminate uncertainty entirely. New data, changing business conditions and evolving user behavior will always introduce risk. But by examining multiple metrics, validating against realistic scenarios and understanding where a model succeeds and where it struggles, teams can make deployment decisions with greater confidence.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-confidence-before-deployment_0":{"id":"text-13ead181a4","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EModel evaluation isn’t about finding a perfect score. It’s about using the right mix of metrics, validation methods and error analysis to understand whether a model’s behavior is suitable for the task it’s meant to support.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-ml-model-evaluation","text_what-is-ml-model-evaluation_0","title_why-ml-model-evaluation-matters","text_why-ml-model-evaluation-matters_0","callout_why-ml-model-evaluation-matters_0","title_why-models-need-metric-portfolios","text_why-models-need-metric-portfolios_0","title_key-ml-model-evaluation-metrics","text_key-ml-model-evaluation-metrics_0","callout_key-ml-model-evaluation-metrics_0","title_model-quality-and-generalization","text_model-quality-and-generalization_0","title_evaluating-modern-llm-systems","text_evaluating-modern-llm-systems_0","title_ml-model-evaluation-and-monitoring-on-snowflake","text_ml-model-evaluation-and-monitoring-on-snowflake_0","title_build-confidence-before-deployment","text_build-confidence-before-deployment_0","callout_build-confidence-before-deployment_0"]},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"container":{"additionalClasses":"sticky-sidebar","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-0f19f0e637",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-0b272b64b1","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn This Guide\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-f2a05ba516","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-ml-model-evaluation\"\u003EWhat is ML model evaluation?\u003C/li\u003E\u003Cli data-anchor=\"why-ml-model-evaluation-matters\"\u003EWhy ML model evaluation matters\u003C/li\u003E\u003Cli data-anchor=\"why-models-need-metric-portfolios\"\u003EWhy models need metric portfolios\u003C/li\u003E\u003Cli data-anchor=\"key-ml-model-evaluation-metrics\"\u003EKey ML model evaluation metrics\u003C/li\u003E\u003Cli data-anchor=\"model-quality-and-generalization\"\u003EModel quality and generalization\u003C/li\u003E\u003Cli data-anchor=\"evaluating-modern-llm-systems\"\u003EEvaluating modern LLM systems\u003C/li\u003E\u003Cli data-anchor=\"ml-model-evaluation-and-monitoring-on-snowflake\"\u003EML model evaluation and monitoring on Snowflake\u003C/li\u003E\u003Cli data-anchor=\"build-confidence-before-deployment\"\u003EBuild confidence before deployment\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"]}},":itemsOrder":["container"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1786318617":{"id":"flexible-column-container-55e74d40fa","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-d7deab728e","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Frequently Asked Questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-6e95ace1ac","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EYour common questions about ML model evaluation, answered by Snowflake experts.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-a62b7ef899","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"What is the difference between model evaluation and model monitoring?","richText":"\u003Cp\u003EModel evaluation measures a model’s performance during development, validation and testing, usually before deployment. Model monitoring tracks model health after deployment by watching for drift, performance degradation, data quality issues, prediction shifts and operational problems in production.\u003C/p\u003E"},{"title":"How do you choose the right model evaluation metric?","richText":"\u003Cp\u003EThe right metric depends on the model’s task, the cost of different error types and the business decision the model supports. Classification models may require precision, recall, F1-score or AUC-ROC, while regression models may use MAE, MSE or RMSE. Teams should usually evaluate multiple metrics rather than relying on a single score.\u003C/p\u003E"},{"title":"What is the difference between validation data and test data?","richText":"\u003Cp\u003EValidation data is typically used during model development to tune models, compare approaches and select thresholds. Test data is held back until later to provide a more independent estimate of final model performance before deployment.\u003C/p\u003E"},{"title":"What is error analysis in model evaluation?","richText":"\u003Cp\u003EError analysis is the process of reviewing incorrect predictions to understand where, how and why a model fails. It helps teams identify patterns across segments, thresholds, data quality issues or edge cases that aggregate metrics may hide.\u003C/p\u003E"},{"title":"How is LLM evaluation different from traditional ML evaluation?","richText":"\u003Cp\u003ETraditional ML evaluation often compares predictions against known labels or target values. 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