{"id":9144669569298,"title":"BigML Delete an Anomaly Score Integration","handle":"bigml-delete-an-anomaly-score-integration","description":"\u003ch2\u003eBigML Delete an Anomaly Score Integration Endpoint Overview\u003c\/h2\u003e\n\n\u003cp\u003eThe BigML API provides a Delete an Anomaly Score Integration endpoint that allows users to remove an anomaly score from their BigML account. An anomaly score is typically used to measure how anomalous a data instance is based on a previously trained anomaly detector. When you delete an anomaly score, you are permanently removing the score and its associated data from your account, freeing up resources and decluttering your workspace.\u003c\/p\u003e\n\n\u003ch2\u003eUse Cases of the Delete Anomaly Score Integration\u003c\/h2\u003e\n\n\u003cp\u003eThe Delete an Anomaly Score Integration endpoint can be used in various scenarios, including:\u003c\/p\u003e\n\n\u003cul\u003e\n \u003cli\u003e\n\u003cstrong\u003eData Management:\u003c\/strong\u003e Over time, users generate many anomaly scores using different anomaly detectors. Some of these scores may become outdated or irrelevant. The delete operation helps maintain organization within the project space.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eResource Optimization:\u003c\/strong\u003e Each anomaly score uses storage on BigML's servers. Deleting unused scores prevents unnecessary resource consumption, which can be particularly relevant in the context of limited storage or when operating within a budget.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003ePrivacy Compliance:\u003c\/strong\u003e In circumstances where data privacy is critical (such as GDPR compliance), it might be necessary to delete data that includes personal information. The delete endpoint facilitates adhering to such regulations.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eError Correction:\u003c\/strong\u003e If an anomaly score was created errone\u003c\/li\u003e\n\u003c\/ul\u003e","published_at":"2024-03-13T01:07:31-05:00","created_at":"2024-03-13T01:07:32-05:00","vendor":"BigML","type":"Integration","tags":[],"price":0,"price_min":0,"price_max":0,"available":true,"price_varies":false,"compare_at_price":null,"compare_at_price_min":0,"compare_at_price_max":0,"compare_at_price_varies":false,"variants":[{"id":48259812917522,"title":"Default Title","option1":"Default Title","option2":null,"option3":null,"sku":"","requires_shipping":true,"taxable":true,"featured_image":null,"available":true,"name":"BigML Delete an Anomaly Score Integration","public_title":null,"options":["Default Title"],"price":0,"weight":0,"compare_at_price":null,"inventory_management":null,"barcode":null,"requires_selling_plan":false,"selling_plan_allocations":[]}],"images":["\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_501bd6b9-d942-4d3d-995a-85de71200cf2.png?v=1710310052"],"featured_image":"\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_501bd6b9-d942-4d3d-995a-85de71200cf2.png?v=1710310052","options":["Title"],"media":[{"alt":"BigML Logo","id":37928036434194,"position":1,"preview_image":{"aspect_ratio":2.121,"height":264,"width":560,"src":"\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_501bd6b9-d942-4d3d-995a-85de71200cf2.png?v=1710310052"},"aspect_ratio":2.121,"height":264,"media_type":"image","src":"\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_501bd6b9-d942-4d3d-995a-85de71200cf2.png?v=1710310052","width":560}],"requires_selling_plan":false,"selling_plan_groups":[],"content":"\u003ch2\u003eBigML Delete an Anomaly Score Integration Endpoint Overview\u003c\/h2\u003e\n\n\u003cp\u003eThe BigML API provides a Delete an Anomaly Score Integration endpoint that allows users to remove an anomaly score from their BigML account. An anomaly score is typically used to measure how anomalous a data instance is based on a previously trained anomaly detector. When you delete an anomaly score, you are permanently removing the score and its associated data from your account, freeing up resources and decluttering your workspace.\u003c\/p\u003e\n\n\u003ch2\u003eUse Cases of the Delete Anomaly Score Integration\u003c\/h2\u003e\n\n\u003cp\u003eThe Delete an Anomaly Score Integration endpoint can be used in various scenarios, including:\u003c\/p\u003e\n\n\u003cul\u003e\n \u003cli\u003e\n\u003cstrong\u003eData Management:\u003c\/strong\u003e Over time, users generate many anomaly scores using different anomaly detectors. Some of these scores may become outdated or irrelevant. The delete operation helps maintain organization within the project space.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eResource Optimization:\u003c\/strong\u003e Each anomaly score uses storage on BigML's servers. Deleting unused scores prevents unnecessary resource consumption, which can be particularly relevant in the context of limited storage or when operating within a budget.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003ePrivacy Compliance:\u003c\/strong\u003e In circumstances where data privacy is critical (such as GDPR compliance), it might be necessary to delete data that includes personal information. The delete endpoint facilitates adhering to such regulations.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eError Correction:\u003c\/strong\u003e If an anomaly score was created errone\u003c\/li\u003e\n\u003c\/ul\u003e"}