{"schema_version":"onlylabs.public_signal.v1","title":"Scaleway Writing: Active Learning, part 1: the Theory","description":"Scaleway writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/6556c940-94ef-4e29-902d-a64f4fa43ca5","json_url":"https://onlylabs.fyi/signals/6556c940-94ef-4e29-902d-a64f4fa43ca5/signal.json","generated_at":"2026-06-07T21:19:27.672144+00:00","org":{"slug":"scaleway","name":"Scaleway","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/scaleway","dossier_json_url":"https://onlylabs.fyi/labs/scaleway/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/6556c940-94ef-4e29-902d-a64f4fa43ca5","signal_json":"https://onlylabs.fyi/signals/6556c940-94ef-4e29-902d-a64f4fa43ca5/signal.json","source":"https://www.scaleway.com/en/blog/active-learning-some-datapoints-are-more-equal-than-others/","lab_dossier":"https://onlylabs.fyi/labs/scaleway","lab_dossier_json":"https://onlylabs.fyi/labs/scaleway/dossier.json","analysis":"https://onlylabs.fyi/analysis/scaleway","analysis_json":"https://onlylabs.fyi/analysis/scaleway/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/scaleway/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Scaleway published Active Learning, part 1: the Theory. 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Active learning is still a relatively niche approach in the machine learning world, but that is bound to change. After all, active learning provides solutions to not one, but two challenging problems that have been poisoning the lives of many a data scientist. I am, of course, talking about the data: namely, its (a) quantity and (b) quality. Let us start with the former. It is no secret that training modern machine learning (ML) models requires large quantities of training data. This is especially true for deep learning (ML carried out via deep artificial neural nets), where it is not uncommon for training sets to number in the hundreds of thousands and beyond. To make matters worse, many practical applications come in the form of supervised ML tasks: i.e., not only do we require all these training samples, but we also need a way to label them. Labeling data is time-consuming and expensive . It is bad enough if it involves human experts manually annotating text or images, but what if labeling entails invasive medical tests to confirm a patient's..."},"evidence_pages":[{"url":"https://www.scaleway.com/en/blog/active-learning-some-datapoints-are-more-equal-than-others/","final_url":"https://www.scaleway.com/en/blog/active-learning-some-datapoints-are-more-equal-than-others/","title":"Active Learning, part 1: the Theory","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:19:27.672144+00:00","bytes":169352,"raw_path":"906c9cf500753fb6ac3d42e4f96b6a3d73a0d5539f97e6f0e233d97257eddd19.html","content_hash":"529988cc04614e9c922662ffa909939c83cafcb8b1c5b7892918a33d8a1a7012","excerpt_chars":1200,"truncated":true,"excerpt":"Active Learning, part 1: the Theory Scale • Olga Petrova • 06/08/20 • 7 min read Active learning: when and why do we need it? Active learning is still a relatively niche approach in the machine learning world, but that is bound to change. After all, active learning provides solutions to not one, but two challenging problems that have been poisoning the lives of many a data scientist. I am, of course, talking about the data: namely, its (a) quantity and (b) quality. Let us start with the former. It is no secret that training modern machine learning (ML) models requires large quantities of training data. This is especially true for deep learning (ML carried out via deep artificial neural nets), where it is not uncommon for training sets to number in the hundreds of thousands and beyond. To make matters worse, many practical applications come in the form of supervised ML tasks: i.e., not only do we require all these training samples, but we also need a way to label them. Labeling data is time-consuming and expensive . 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