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Tracking","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-8a809fcb7e","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-7c16e077a2","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-ccb0703776","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Experiment Tracking in MLOps: Compare, Evaluate and Reproduce ML Models"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-b19b301444","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EAs machine learning workflows become more iterative, experiment tracking helps teams separate meaningful improvements from changes in data, code or configuration. By preserving the context behind each result, it turns a series of model runs into evidence teams can compare, investigate and build on.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-e0b6432d68","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"content_chip":{"id":"content-chip-eb15dd0802","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read bio"},"image":{"id":"image","lazyEnabled":true,"alt":"Laurie MacPherson","isLcpImage":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?preferwebp=true&quality=85","height":"800","width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-d548910a47","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read bio"},"image":{"id":"image","lazyEnabled":true,"alt":"David Gaule","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fd9454ea-3b59-4d19-95be-534774cbd226/david.jpg?preferwebp=true&quality=85","height":"512","width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],":type":"snowflake-site/components/container"}},":itemsOrder":["title_v2","text","container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-925212ffce",":items":{"youtube":{"id":"embed-2b17f6d450","youtubeVideoId":"WFR07HIvCrQ","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"}},":itemsOrder":["youtube"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-ee8bfe5cda","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-01b0a0d206",":items":{"text_894059747":{"id":"text-8df088cf4f","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":{"id":"text-886a7d86f9","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/model-evaluation/\"\u003EML Model Evaluation\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-training/\"\u003EML Model Training\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/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_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-f7cf958ae8","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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout__0":{"id":"text-435f8dbbe8","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EEXPERIMENT TRACKING DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EExperiment tracking is the practice of maintaining a structured history of model-development activity so teams can trace each result back to the choices and resources behind it.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-7104214331","text":"\u003Cp\u003EWhat happens when one \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E run performs noticeably better than the others, but by the time the team reviews it, no one can reconstruct the exact settings used? The team no longer knows whether the higher score came from a meaningful improvement, a difference in the data or a configuration change.\u003C/p\u003E\n\u003Cp\u003EExperiment tracking records the conditions used to train a \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/models/\"\u003Emodel\u003C/a\u003E — the inputs, configuration and outputs associated with each run — so teams can compare results, investigate performance differences and reproduce a promising result later. Instead of relying on filenames, notebooks or manually maintained spreadsheets, practitioners get a searchable record that connects a metric to the code, data, parameters and artifacts that produced it.\u003C/p\u003E\n\u003Cp\u003EThis makes experiment tracking a core machine learning operations (\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E) practice. Model development is iterative: practitioners adjust hyperparameters, revise preprocessing logic, test features and retrain against new versions of a data set. Each change creates another run, and each run adds evidence about which approach works best. Experiment tracking gives teams a consistent way to evaluate model candidates and investigate unexpected results.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-experiment-tracking":{"id":"title-v2-b660947397","additionalClasses":"anchor-title anchor-title--what-is-experiment-tracking","type":"heading2","lines":["What is experiment tracking?"],":type":"snowflake-site/components/title-v2"},"text_what-is-experiment-tracking_0":{"id":"text-2db4c42e6b","text":"\u003Cp\u003EExperiment tracking is the systematic logging of ML runs throughout model development. A tracking system typically captures parameters and hyperparameters, evaluation metrics, code and data versions, environment details and artifacts such as trained models, checkpoints, plots and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/model-evaluation/\"\u003Emodel evaluation\u003C/a\u003E reports.\u003C/p\u003E\n\u003Cp\u003EThe basic unit is a run: one execution of a model-development workflow under a defined configuration. Related runs are grouped into an experiment, allowing teams to compare how changes to the model, data, features or training settings affect the result. An experiment might include runs that use different algorithms, training windows or learning rates.\u003C/p\u003E\n\u003Cp\u003EMany tools automate part of this process through autologging, which records common parameters, metrics and artifacts directly from supported training frameworks. Teams can then search, filter and compare runs in a tracking interface rather than reconstructing results from notebooks, filenames or manually maintained notes.\u003C/p\u003E\n\u003Cp\u003EThe resulting record supports both comparison and reproducibility. It shows which configuration produced a given metric, what changed between runs and which code and data versions were involved.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-to-track-in-a-machine-learning-experiment":{"id":"title-v2-0a88c96eb7","additionalClasses":"anchor-title anchor-title--what-to-track-in-a-machine-learning-experiment","type":"heading2","lines":["What to track in a machine learning experiment"],":type":"snowflake-site/components/title-v2"},"text_what-to-track-in-a-machine-learning-experiment_0":{"id":"text-fbb836dc30","text":"\u003Cp\u003EThe information tracked for each experiment depends on the modeling problem and the variables being tested. A team developing a demand forecasting model, for example, might create an experiment containing runs with different algorithms, feature sets, training windows and hyperparameter values.\u003C/p\u003E\n\u003Cp\u003EAn experiment tracking system typically records several types of information for each run:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EParameters and hyperparameters:\u003C/strong\u003E Settings such as learning rate, batch size, tree depth or number of training epochs explain how the model was configured.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEvaluation metrics:\u003C/strong\u003E Measures such as accuracy, precision, recall, F1 score, root mean square error (RMSE) or validation loss show how the model performed against defined criteria.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECode, model and dependency identifiers:\u003C/strong\u003E A source commit, model architecture or base-model identifier, and relevant package versions tie the result to the implementation used.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EData set version:\u003C/strong\u003E A reference to the training and validation data prevents teams from treating results produced from different data as directly comparable.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEnvironment details:\u003C/strong\u003E Library versions, runtime configuration and hardware information can affect performance and reproducibility.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EArtifacts:\u003C/strong\u003E Files generated during training might include a serialized model, checkpoints, evaluation reports, plots, confusion matrices, tokenizers or preprocessing objects.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-to-track-in-a-machine-learning-experiment_0":{"id":"card-v2-096c7f2263","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Coinbase"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/video/coinbase/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Watch the customer story"},"image":{"id":"image","lazyEnabled":true,"alt":"Coinbase","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--bc2d7ba0-ef6c-4dbe-921b-3898970c8aa4/coinbase.png?preferwebp=true&quality=85","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ECoinbase uses Snowflake ML to simplify machine learning workflows and help data scientists move faster without sending data across separate platforms. By bringing feature engineering, model training, deployment and refreshes into Snowflake, Coinbase reported reducing ML workflow timelines from months to days or hours, improved productivity and governance, and enabled more timely insights for use cases such as product personalization, customer retention and platform experience [as of August 2025].\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_how-experiment-tracking-supports-reproducibility-and-collaboration":{"id":"title-v2-02260f77fd","additionalClasses":"anchor-title anchor-title--how-experiment-tracking-supports-reproducibility-and-collaboration","type":"heading2","lines":["How experiment tracking supports reproducibility and collaboration"],":type":"snowflake-site/components/title-v2"},"text_how-experiment-tracking-supports-reproducibility-and-collaboration_0":{"id":"text-bd01749d02","text":"\u003Cp\u003EThe same training code can produce different results because of changes in data, dependencies, parameters, random seeds or nondeterministic hardware and framework behavior. Experiment tracking reduces ambiguity by associating each result with its training context. If a run achieves an unexpectedly strong result, teams can trace it back to the relevant code revision, data set version, configuration and artifacts. They can rerun the experiment under comparable conditions or isolate the variable that appears to have influenced performance.\u003C/p\u003E\n\u003Cp\u003EThat record also supports collaboration. A data scientist reviewing another practitioner’s work doesn’t have to infer which notebook cell ran last or which local file supplied the training data. The experiment history provides a shared reference for discussing results, reviewing changes and handing work to another team.\u003C/p\u003E\n\u003Cp\u003EReproducibility isn’t automatic, however. An incomplete record can leave important gaps. Teams generally need consistent logging conventions, versioned data and code, captured dependencies, and stable identifiers that connect those components. Autologging reduces manual work, but custom preprocessing steps, external services or application-specific evaluation logic may still require explicit instrumentation.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-experiment-tracking-supports-reproducibility-and-collaboration_0":{"id":"text-713a3f11ee","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ETeams sometimes capture model hyperparameters but overlook preprocessing, feature logic or retrieval configuration. Remember that changes outside the model itself can have an equally large effect on the result.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_experiment-tracking-vs-model-registries-and-model-monitoring":{"id":"title-v2-5ebf7cffc9","additionalClasses":"anchor-title anchor-title--experiment-tracking-vs-model-registries-and-model-monitoring","type":"heading2","lines":["Experiment tracking vs. model registries and model monitoring"],":type":"snowflake-site/components/title-v2"},"text_experiment-tracking-vs-model-registries-and-model-monitoring_0":{"id":"text-07929f0668","text":"\u003Cp\u003EExperiment tracking, model registries and model monitoring operate at different points in the ML lifecycle.\u003C/p\u003E\n\u003Cp\u003EDuring development, experiment tracking records the runs used to test possible approaches. A single experiment may contain dozens or hundreds of model candidates, most of which will never move beyond evaluation.\u003C/p\u003E\n\u003Cp\u003EOnce a team selects a model, a model registry provides a versioned record for that model. The registry can associate the model with metadata such as evaluation results, ownership, approval status and deployment stage. Experiment tracking preserves the development history, while the registry manages the smaller set of models chosen for further use.\u003C/p\u003E\n\u003Cp\u003EAfter deployment, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/model-monitoring/\"\u003Emodel monitoring\u003C/a\u003E evaluates how the model behaves in production. It may surface changes in input distributions, prediction quality, latency or other operational measures. Those signals can trigger another development cycle, at which point practitioners can use experiment tracking to evaluate retraining or remediation options.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_experiment-tracking-for-modern-ai-development":{"id":"title-v2-74ed54554f","additionalClasses":"anchor-title anchor-title--experiment-tracking-for-modern-ai-development","type":"heading2","lines":["Experiment tracking for modern AI development"],":type":"snowflake-site/components/title-v2"},"text_experiment-tracking-for-modern-ai-development_0":{"id":"text-bc941e1faf","text":"\u003Cp\u003EThe same underlying practice applies beyond conventional supervised ML. For \u003Ca href=\"https://www.snowflake.com/en/fundamentals/large-language-model/\"\u003Elarge language model\u003C/a\u003E applications, the tracked configuration may include the prompt or template version, decoding settings, evaluator version and the specific model or provider endpoint used. For an \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/feature-engineering/embeddings/\"\u003Eembedding\u003C/a\u003E model, it could include model version, chunking strategy and retrieval metrics. Reinforcement learning experiments typically capture reward functions and policy checkpoints, while multimodal training introduces data transformations and evaluation artifacts for several input types.\u003C/p\u003E\n\u003Cp\u003EEvaluation-heavy applications may also track changes that occur outside model weights. A retrieval-augmented generation system, for example, can produce different results when teams change the retrieval method, embedding model, chunk size, ranking configuration or evaluation data set. Recording those choices gives practitioners a clearer basis for comparing system configurations.\u003C/p\u003E\n\u003Cp\u003EWhatever the object being evaluated, the governing question remains the same: What combination of inputs, settings and implementation choices produced this result?\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_experiment-tracking-for-modern-ai-development_0":{"id":"text-149e14f594","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ETrack the entire evaluated system, not only the model. For a RAG application, that may include the source corpus, embedding model, chunking method, retrieval settings, prompt and evaluator version.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_experiment-tracking-tools":{"id":"title-v2-e87d99a6a9","additionalClasses":"anchor-title anchor-title--experiment-tracking-tools","type":"heading2","lines":["Experiment tracking tools"],":type":"snowflake-site/components/title-v2"},"text_experiment-tracking-tools_0":{"id":"text-222c580952","text":"\u003Cp\u003E\u003Ca href=\"https://mlflow.org/\" target=\"_blank\"\u003EMLflow\u003C/a\u003E is a widely used open source platform for experiment tracking. Its tracking components organize work around runs, parameters, metrics and artifacts, with APIs and autologging support for common ML frameworks.\u003C/p\u003E\n\u003Cp\u003ECommercial platforms provide related capabilities, often alongside visualization, collaboration and model development workflows. Teams may also build experiment metadata capture into broader ML platforms or internal development systems.\u003C/p\u003E\n\u003Cp\u003EThe right implementation depends partly on the surrounding architecture. A tracking system must integrate with the training environment, preserve the metadata practitioners need and make run history accessible to the people responsible for evaluating models. Consistent use matters as much as tool selection: a sophisticated platform can’t support reproducibility when individual projects log different fields or omit critical data and code references.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_experiment-tracking-on-snowflake":{"id":"title-v2-935ba64c04","additionalClasses":"anchor-title anchor-title--experiment-tracking-on-snowflake","type":"heading2","lines":["Experiment tracking on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_experiment-tracking-on-snowflake_0":{"id":"text-df065ce131","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E supports experiment tracking for ML development performed with governed data in Snowflake. Practitioners can capture parameters, metrics and artifacts associated with training runs, including runs created during hyperparameter tuning, then compare results as they evaluate model configurations.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake also supports integration with MLflow, allowing teams to use familiar tracking workflows while training against data governed in Snowflake. Once a model has been selected, \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/model-management\"\u003ESnowflake Model Registry\u003C/a\u003E can store and version the resulting model alongside relevant metadata and metrics.\u003C/p\u003E\r\n\u003Cp\u003EThis creates a connected path from experimentation to model management without relying on experiment tracking to be a substitute for either \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Edata governance\u003C/a\u003E or the registry itself. The tracking record documents how candidate models were produced, while the registry manages the models a team decides to retain and use.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_experiment-tracking-turns-runs-into-evidence":{"id":"title-v2-d901f9106e","additionalClasses":"anchor-title anchor-title--experiment-tracking-turns-runs-into-evidence","type":"heading2","lines":["Experiment tracking turns runs into evidence"],":type":"snowflake-site/components/title-v2"},"text_experiment-tracking-turns-runs-into-evidence_0":{"id":"text-2d4858ae4c","text":"\u003Cp\u003EML development is inherently experimental. Teams evaluate different model architectures, adjust hyperparameters, revise preprocessing pipelines and retrain against new versions of a data set, often producing dozens or hundreds of runs before selecting a model for production. Without a reliable record of those iterations, it becomes difficult to explain why one approach performed better than another or to build confidently on previous work.\u003C/p\u003E\n\u003Cp\u003EExperiment tracking gives practitioners that record. By connecting every metric to the code, data, configuration and artifacts that produced it, it turns individual training runs into evidence that teams can compare, validate and reproduce. As organizations scale their ML and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI\u003C/a\u003E initiatives, that evidence becomes the foundation for reproducible development, effective collaboration and trustworthy MLOps.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_experiment-tracking-turns-runs-into-evidence_0":{"id":"text-36d6710de2","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EExperiment tracking connects each result to the data, code, configuration and artifacts that produced it. That record helps teams compare approaches, investigate unexpected outcomes and reproduce promising runs, but its value depends on capturing the right context consistently.\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-experiment-tracking","text_what-is-experiment-tracking_0","title_what-to-track-in-a-machine-learning-experiment","text_what-to-track-in-a-machine-learning-experiment_0","card_v2_what-to-track-in-a-machine-learning-experiment_0","title_how-experiment-tracking-supports-reproducibility-and-collaboration","text_how-experiment-tracking-supports-reproducibility-and-collaboration_0","callout_how-experiment-tracking-supports-reproducibility-and-collaboration_0","title_experiment-tracking-vs-model-registries-and-model-monitoring","text_experiment-tracking-vs-model-registries-and-model-monitoring_0","title_experiment-tracking-for-modern-ai-development","text_experiment-tracking-for-modern-ai-development_0","callout_experiment-tracking-for-modern-ai-development_0","title_experiment-tracking-tools","text_experiment-tracking-tools_0","title_experiment-tracking-on-snowflake","text_experiment-tracking-on-snowflake_0","title_experiment-tracking-turns-runs-into-evidence","text_experiment-tracking-turns-runs-into-evidence_0","callout_experiment-tracking-turns-runs-into-evidence_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 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-859b13c0cc","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-06085471ca","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-7826f0563f","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-experiment-tracking\"\u003EWhat is experiment tracking?\u003C/li\u003E\u003Cli data-anchor=\"what-to-track-in-a-machine-learning-experiment\"\u003EWhat to track in a machine learning experiment\u003C/li\u003E\u003Cli data-anchor=\"how-experiment-tracking-supports-reproducibility-and-collaboration\"\u003EHow experiment tracking supports reproducibility and collaboration\u003C/li\u003E\u003Cli data-anchor=\"experiment-tracking-vs-model-registries-and-model-monitoring\"\u003EExperiment tracking vs. model registries and model monitoring\u003C/li\u003E\u003Cli data-anchor=\"experiment-tracking-for-modern-ai-development\"\u003EExperiment tracking for modern AI development\u003C/li\u003E\u003Cli data-anchor=\"experiment-tracking-tools\"\u003EExperiment tracking tools\u003C/li\u003E\u003Cli data-anchor=\"experiment-tracking-on-snowflake\"\u003EExperiment tracking on Snowflake\u003C/li\u003E\u003Cli data-anchor=\"experiment-tracking-turns-runs-into-evidence\"\u003EExperiment tracking turns runs into evidence\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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Questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-961b32c1d1","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EYour common questions about experiment tracking, 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-15cea46493","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"What is the difference between an experiment and a training run?","richText":"\u003Cp\u003EA training run is one execution of a model training or evaluation process. An experiment groups related runs so practitioners can compare different configurations used to address the same modeling problem.\u003C/p\u003E"},{"title":"What should be logged during every ML experiment?","richText":"\u003Cp\u003EExperiment tracking should generally record a training run’s parameters, evaluation metrics, code version, data set version, environment details and relevant artifacts. The exact record depends on the model and training workflow.\u003C/p\u003E"},{"title":"Why is experiment tracking important for reproducibility?","richText":"\u003Cp\u003EExperiment tracking links a result to the conditions that produced it. That record helps teams recreate a run, investigate performance differences and determine which changes influenced the outcome.\u003C/p\u003E"},{"title":"Does experiment tracking replace a model registry?","richText":"\u003Cp\u003ENo, experiment tracking records many development runs, while a model registry stores and manages the smaller set of model versions a team chooses to retain, approve or 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