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href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-fairness/\"\u003EAI Fairness\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EAI Risk Management\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003EAI Traceability\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003EAI Transparency\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EAlgorithmic Bias\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EEU AI Act\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO 42001\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/model-governance/\"\u003EModel Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003EResponsible 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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EMODEL CARD DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EA model card is a structured record that explains how an AI model was developed, evaluated and intended to be used. It gives stakeholders the context they need to understand a model’s capabilities, limitations and risks before deploying it.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-3f6791ccb0","text":"\u003Cp\u003EAs \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI systems\u003C/a\u003E move from development into production, the teams responsible for deploying them often lack the context to assess whether an AI model fits their specific workflow. A model’s benchmark results don’t capture where it was evaluated, which populations it was tested on or where it’s likely to underperform. Without that information, deployment decisions rely on assumption rather than evidence — and the consequences fall on the teams, users and processes downstream.\u003C/p\u003E\n\u003Cp\u003EModel cards address this problem directly. A model card documents what a model is, what it was trained and evaluated on, how it performs across relevant conditions, where it should not be used and what risks reviewers need to understand. The documentation serves a coordination function as AI moves deeper into production. Data scientists, risk reviewers, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-compliance/\"\u003Ecompliance\u003C/a\u003E teams and downstream integrators rarely share context, and a model card gives them a common artifact for reference.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-model-card":{"id":"title-v2-425dcb2ccb","additionalClasses":"anchor-title anchor-title--what-is-a-model-card","type":"heading2","lines":["What is a model card?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-model-card_0":{"id":"text-d960b8b269","text":"\u003Cp\u003EA model card is a standardized document that describes an AI or ML model’s purpose, training data, performance characteristics, intended uses, known limitations, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-fairness/\"\u003Efairness\u003C/a\u003E considerations and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-ethics/\"\u003Eethical considerations\u003C/a\u003E. It gives reviewers a consistent reference for deciding whether a model is appropriate for a given workflow. Model cards help operationalize a \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003Eresponsible AI\u003C/a\u003E commitment.\u003C/p\u003E\n\u003Cp\u003EThe concept was introduced in \u003Ca href=\"https://arxiv.org/abs/1810.03993\" target=\"_blank\"\u003EModel Cards for Model Reporting,\u003C/a\u003E a 2019 ACM paper co-authored by AI researcher Margaret Mitchell and fellow researchers. The paper defines model cards as short documents that accompany trained ML models and report benchmarked evaluation across conditions relevant to the model’s intended application, including demographic, cultural, phenotypic and intersectional groups where appropriate.\u003C/p\u003E\n\u003Cp\u003EA model card is sometimes compared to a nutrition label, but for enterprise teams the closer analogy is a software release artifact: a versioned record tied to a specific model version so reviewers can see its intended use, evaluation data, disaggregated performance, caveats and recommended deployment practices.\u003C/p\u003E\n\u003Cp\u003EA model card is different from a datasheet for data sets. A datasheet documents a data set’s motivation, composition, collection process and recommended uses. A model card documents the model trained or evaluated with that data, including how it performs and where it should or should not be used.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-9-canonical-sections-of-a-model-card":{"id":"title-v2-c0c0b391cb","additionalClasses":"anchor-title anchor-title--the-9-canonical-sections-of-a-model-card","type":"heading2","lines":["The 9 canonical sections of a model card"],":type":"snowflake-site/components/title-v2"},"text_the-9-canonical-sections-of-a-model-card_0":{"id":"text-17ee599960","text":"\u003Cp\u003EMitchell et al. proposed a model card structure that gives teams a consistent way to report model details, intended use, performance and limitations. The exact format may vary by organization, model type and risk level, but the canonical sections remain a useful foundation.\u003C/p\u003E\n\u003Ch3\u003E1. Model details\u003C/h3\u003E\n\u003Cp\u003EThe model details section identifies the model and the team responsible for it. It typically includes the model name, version, release date, model type, license, citation or paper reference, owner and contact information.\u003C/p\u003E\n\u003Cp\u003EWithout a version, owner or date, a model card isn’t as useful since reviewers can’t tell which model it describes, whether it reflects the current deployed version or whom to contact when performance changes.\u003C/p\u003E\n\u003Ch3\u003E2. Intended use\u003C/h3\u003E\n\u003Cp\u003EThe intended use section describes what the model is designed to do, who’s expected to use it and which use cases fall outside its intended scope. A model designed to classify support tickets for routing is not necessarily appropriate for eligibility decisions or legally binding responses.\u003C/p\u003E\n\u003Cp\u003EOut-of-scope uses should be stated directly. A model’s risk often increases when it’s reused in a workflow that resembles its original context but carries different data, users, consequences or regulatory obligations.\u003C/p\u003E\n\u003Ch3\u003E3. Factors\u003C/h3\u003E\n\u003Cp\u003EThe factors section identifies conditions that may influence model performance: demographic factors, environmental conditions, data collection methods, instrumentation differences, language variation, geography, device type or other context-specific variables.\u003C/p\u003E\n\u003Cp\u003EFor a computer vision model, relevant factors might include lighting, image resolution or skin tone. For a language model, they could be dialect, domain vocabulary or document format. For an enterprise forecasting model, those factors might include region, seasonality, product category or customer segment.\u003C/p\u003E\n\u003Ch3\u003E4. Metrics\u003C/h3\u003E\n\u003Cp\u003EThe metrics section explains how the model was evaluated. It may include accuracy, precision, recall, F1 score, false positive rate, false negative rate, latency, calibration, robustness measures or task-specific metrics.\u003C/p\u003E\n\u003Cp\u003EAggregate performance numbers are not enough. The metrics section should explain why the selected metrics matter for the use case, which thresholds were applied and how those thresholds affect downstream decisions.\u003C/p\u003E\n\u003Ch3\u003E5. Evaluation data\u003C/h3\u003E\n\u003Cp\u003EThe evaluation data section describes the datasets used to test the model — where they came from, why they were selected, how they were preprocessed and whether they reflect the model’s intended deployment environment.\u003C/p\u003E\n\u003Cp\u003EA model evaluated on clean, balanced or synthetic data may behave differently against production data with missing values, class imbalance, ambiguous labels or distribution drift.\u003C/p\u003E\n\u003Ch3\u003E6. Training data\u003C/h3\u003E\n\u003Cp\u003EThe training data section describes the data sets used to train the model: source, motivation, preprocessing steps, inclusion and exclusion criteria, and known limitations. When a separate datasheet exists for the training data set, the model card can reference it rather than duplicate the detail.\u003C/p\u003E\n\u003Cp\u003EFor foundation models or models trained on large data collections, this section may need to cover the training mix, data cutoffs, filtering practices and categories of excluded content. For fine-tuned models, it should distinguish the base model from the fine-tuning data and describe how fine-tuning changes expected behavior.\u003C/p\u003E\n\u003Ch3\u003E7. Quantitative analyses\u003C/h3\u003E\n\u003Cp\u003EThe quantitative analyses section breaks performance down across relevant factors. This is where teams report disaggregated performance, subgroup analysis and intersectional analysis.\u003C/p\u003E\n\u003Cp\u003EAggregate accuracy can look acceptable while performance varies by language, region, device type or demographic group. Reporting those differences shows deployers where additional testing, monitoring or mitigation may be needed.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-9-canonical-sections-of-a-model-card_0":{"id":"text-565820d63c","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EA model card is only as useful as the evidence behind it. Teams often rely on high-level performance claims or aggregate metrics while omitting limitations, subgroup results or out-of-scope uses — leaving reviewers without the information needed to assess deployment risk.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_the-9-canonical-sections-of-a-model-card_1":{"id":"text-da431f2bbe","text":"\u003Ch3\u003E8. Ethical considerations\u003C/h3\u003E\n\u003Cp\u003EThe ethical considerations section describes sensitive use cases, potential harms, bias considerations, misuse risks and mitigation steps. It should be grounded in the model’s actual context, not written as a generic risk paragraph.\u003C/p\u003E\n\u003Cp\u003EFor example, for a hiring model, relevant considerations include demographic bias, proxy variables and human review requirements. For a healthcare model, considerations include patient safety, population representativeness and escalation paths. For a generative AI application, considerations include hallucination, overreliance, privacy leakage, unsafe outputs and prompt injection.\u003C/p\u003E\n\u003Ch3\u003E9. Caveats and recommendations\u003C/h3\u003E\n\u003Cp\u003EThe caveats and recommendations section gives deployers practical guidance, such as known limitations, open questions, required monitoring, recommended human oversight, unsuitable contexts, retraining triggers and additional tests to run before deployment.\u003C/p\u003E\n\u003Cp\u003EThis is where the model card is especially valuable to users beyond the development team. A caveat such as “not evaluated on documents longer than 20 pages” or “requires human review for account closure recommendations” gives downstream teams a concrete boundary they can design around.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_when-to-publish-a-model-card-and-what-level-of-detail-to-include":{"id":"title-v2-23f6eecf2b","additionalClasses":"anchor-title anchor-title--when-to-publish-a-model-card-and-what-level-of-detail-to-include","type":"heading2","lines":["When to publish a model card and what level of detail to include"],":type":"snowflake-site/components/title-v2"},"text_when-to-publish-a-model-card-and-what-level-of-detail-to-include_0":{"id":"text-d4c45f2902","text":"\u003Cp\u003ENot every model needs the same level of documentation. A low-risk internal analytics model, a customer-facing model and a foundation model carry different obligations, audiences and update patterns. The card should match the model’s deployment context and potential impact.\u003C/p\u003E\r\n\u003Ch3\u003EInternal-only\u003C/h3\u003E\r\n\u003Cp\u003EFor internal-only models, a lightweight card may be sufficient. A short record covering model purpose, owner, training data, evaluation data, monitoring approach and review date prevents the model from becoming an undocumented dependency inside a dashboard, pipeline or operational process.\u003C/p\u003E\r\n\u003Ch3\u003ECustomer-facing\u003C/h3\u003E\r\n\u003Cp\u003ECustomer-facing or product-embedded models need fuller documentation. When a model affects user experience, customer workflows or regulated business processes, reviewers need the following information: intended use, out-of-scope use cases, performance by relevant factor, evaluation data, known limitations, human oversight expectations and a changelog.\u003C/p\u003E\r\n\u003Ch3\u003EFoundation models\u003C/h3\u003E\r\n\u003Cp\u003EFoundation models and fine-tuned variants typically require extended documentation. A foundation model card may include training cutoff dates, safety evaluations, red-team findings, jailbreak resistance, content limitations, model behavior under adversarial prompts and guidance for downstream developers. OpenAI, Google DeepMind and other model providers publish system cards or model cards following this broader documentation pattern for major releases.\u003C/p\u003E\r\n\u003Ch3\u003EHigh-risk systems\u003C/h3\u003E\r\n\u003Cp\u003EHigh-risk AI systems may also need documentation that supports regulatory obligations. Under the \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EEU AI Act\u003C/a\u003E, providers of high-risk AI systems must prepare technical documentation before placing a system on the market or putting it into service. Annex IV specifies what that documentation must cover: intended purpose, provider, version, development process, data requirements, testing procedures and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-risk-management/\"\u003Erisk management\u003C/a\u003E measures.\u003C/p\u003E\r\n\u003Cp\u003EVersioning is essential. Every retraining, fine-tuning event, material evaluation change or deployment-impacting update should produce a new card version or a documented update to the existing one. The card should include date, model version, change summary and reviewing owner so teams can connect model behavior to the artifact that describes it.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_when-to-publish-a-model-card-and-what-level-of-detail-to-include_0":{"id":"text-3db19a5f50","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ETreat your model card as a living document. Update it whenever a model is retrained, fine-tuned or deployed in a new context.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_model-card-best-practices-and-common-failure-modes":{"id":"title-v2-ba2aa16982","additionalClasses":"anchor-title anchor-title--model-card-best-practices-and-common-failure-modes","type":"heading2","lines":["Model card best practices and common failure modes"],":type":"snowflake-site/components/title-v2"},"text_model-card-best-practices-and-common-failure-modes_0":{"id":"text-224b4ce6fb","text":"\u003Cp\u003EThe strongest cards are written for the people deploying the model. They describe what the model can do, where it was tested, where it may fail and what a team should verify before relying on it.\u003C/p\u003E\n\u003Cp\u003EStrong model cards typically do the following:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EQuantify performance using metrics that match the use case\u003C/li\u003E\n\u003Cli\u003EDisaggregate performance by relevant factors\u003C/li\u003E\n\u003Cli\u003EState out-of-scope uses directly\u003C/li\u003E\n\u003Cli\u003ELink to the model registry entry, training data documentation and evaluation data\u003C/li\u003E\n\u003Cli\u003EInclude an owner, date, version and changelog\u003C/li\u003E\n\u003Cli\u003EDocument review expectations after retraining or material changes\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECommon failure modes are just as important to consider:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EMarketing copy standing in for documentation doesn’t help a reviewer assess deployment risk.\u003C/li\u003E\n\u003Cli\u003EPlaceholders, no subgroup breakdown, no explicit out-of-scope statement and no contact for questions make the document unusable.\u003C/li\u003E\n\u003Cli\u003EAggregate-only metrics are especially risky because they can hide uneven performance across groups, environments or data conditions.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ETemplates and model card generators can help teams capture metadata, organize metrics and avoid blank-page documentation work. But the operational aspect shouldn’t be overlooked: assigning an owner, linking the card to the right model version, updating it after retraining and making sure reviewers can trust the card as part of the release process.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ELearn how to create a model in a notebook, save the model to the Snowflake model registry and deploy the model via SQL:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_model-card-best-practices-and-common-failure-modes_0":{"id":"embed-5aaf6b890e","youtubeVideoId":"9cy8eEjXQYg","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_model-cards-on-snowflake":{"id":"title-v2-ddd41c2f4b","additionalClasses":"anchor-title anchor-title--model-cards-on-snowflake","type":"heading2","lines":["Model cards on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_model-cards-on-snowflake_0":{"id":"text-032bd1bf9f","text":"\u003Cp\u003EModel cards are easier to maintain when the information behind them lives close to the data, models and governance controls they describe. \u003Ca href=\"https://www.snowflake.com/en/why-snowflake/what-is-data-cloud/\"\u003ESnowflake’s AI Data Cloud\u003C/a\u003E is designed to give teams a way to manage data and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003EAI governance\u003C/a\u003E in a common environment, so model documentation can draw from governed metadata rather than depend on manual updates.\u003C/p\u003E\n\u003Cp\u003ESnowflake Model Registry provides a way to manage models and their metadata in Snowflake, regardless of origin and type. It supports model versions, artifacts, metadata and inference workflows, giving teams a place to connect a model card to the model version it documents.\u003C/p\u003E\n\u003Cp\u003EThat versioned registry context is valuable because a model card must describe a specific model version: the training data used, evaluation data selected, metrics reported, intended use approved, caveats recorded and monitoring owner assigned at a point in time.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E can also support model card maintenance by surfacing governance context around data assets, including definitions, lineage and policy behavior. Lineage capabilities let users inspect supported objects and trace upstream or downstream dependencies. For model cards, lineage and metadata context helps teams document which governed data assets were used for training or evaluation and how those assets relate to downstream uses.\u003C/p\u003E\n\u003Cp\u003ESnowflake Cortex AI includes AI observability capabilities for evaluating generative AI applications and agents, with metrics such as accuracy, latency, usage and cost. Snowflake also supports semantic views, allowing teams to run verified queries, inspect evaluation runs and track regressions over time. These outputs can serve as evidence behind the model card’s metrics, caveats and recommendations sections.\u003C/p\u003E\n\u003Cp\u003EAdditionally, \u003Ca href=\"https://www.snowflake.com/en/blog/ISO-IEC-42001-AI-certification/\"\u003ESnowflake’s ISO/IEC 42001 certification\u003C/a\u003E reflects an independently assessed AI management system and governance framework.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_model-cards-make-models-usable":{"id":"title-v2-4d34c998f8","additionalClasses":"anchor-title anchor-title--model-cards-make-models-usable","type":"heading2","lines":["Model cards make models usable"],":type":"snowflake-site/components/title-v2"},"text_model-cards-make-models-usable_0":{"id":"text-15872d0e43","text":"\u003Cp\u003EA model card is not merely a compliance checkbox. It provides the documentation that makes a model usable by the reviewers, deployers and risk owners who need to understand what it does, where it was tested and where it should not go.\u003C/p\u003E\n\u003Cp\u003EThe investment in implementing model cards becomes obvious at scale. As organizations deploy more models across more workflows, the ability to assess any one model quickly — its provenance, its performance across conditions, its known limits — becomes an operational requirement. Model cards are how that assessment stays grounded in evidence.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_model-cards-make-models-usable_0":{"id":"text-81872bae80","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EModel cards turn AI models from black boxes into documented, reviewable systems by capturing how they were trained, tested and intended to be used. As organizations deploy more AI across business-critical workflows, model cards support \u003Ca href=\"https://www.snowflake.com/en/fundamentals/artificial-intelligence/ai-governance/model-governance/\"\u003Emodel governance\u003C/a\u003E by providing the \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etransparency\u003C/a\u003E, accountability and governance needed to evaluate models responsibly and deploy them with confidence.\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-a-model-card","text_what-is-a-model-card_0","title_the-9-canonical-sections-of-a-model-card","text_the-9-canonical-sections-of-a-model-card_0","callout_the-9-canonical-sections-of-a-model-card_0","text_the-9-canonical-sections-of-a-model-card_1","title_when-to-publish-a-model-card-and-what-level-of-detail-to-include","text_when-to-publish-a-model-card-and-what-level-of-detail-to-include_0","callout_when-to-publish-a-model-card-and-what-level-of-detail-to-include_0","title_model-card-best-practices-and-common-failure-modes","text_model-card-best-practices-and-common-failure-modes_0","yt_model-card-best-practices-and-common-failure-modes_0","title_model-cards-on-snowflake","text_model-cards-on-snowflake_0","title_model-cards-make-models-usable","text_model-cards-make-models-usable_0","callout_model-cards-make-models-usable_0"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"container":{"additionalClasses":"sticky-sidebar","gridClassNames":"aem-Grid 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card and what level of detail to include\u003C/li\u003E\u003Cli data-anchor=\"model-card-best-practices-and-common-failure-modes\"\u003EModel card best practices and common failure modes\u003C/li\u003E\u003Cli data-anchor=\"model-cards-on-snowflake\"\u003EModel cards on Snowflake\u003C/li\u003E\u003Cli data-anchor=\"model-cards-make-models-usable\"\u003EModel cards make models usable\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"],":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_1786318617":{"id":"flexible-column-container-11545b806b","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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-2cdd8e89d4","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Frequently Asked Questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-c9667b93bd","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EYour common questions about model cards, 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"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-169f848c00","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"What’s the difference between a model card and a datasheet?","richText":"\u003Cp\u003EA datasheet documents a data set — how it was created, what it contains, how it was collected, what it should be used for and what limitations it carries. A model card documents the model trained or evaluated with that data, including how it performs and where it should or should not be used.\u003C/p\u003E"},{"title":"Are model cards required by law?","richText":"\u003Cp\u003ESome AI regulations and governance frameworks require technical documentation, transparency, risk management evidence or evaluation records that a model card can help organize. For high-risk AI systems under the EU AI Act, providers must prepare technical documentation before the system is placed on the market or put into service, and Annex IV specifies the types of information that documentation must include.\u003C/p\u003E"},{"title":"How often should I update a model card?","richText":"\u003Cp\u003EUpdate a model card whenever the model changes in a way that could affect performance, risk or appropriate use. Common triggers include retraining, fine-tuning, a new model version, new evaluation data, a material performance change, a new deployment context, a policy change or a newly identified limitation. At minimum, each production model should have a current card version, a review date and a changelog that explains what changed and 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