Telegram RegisterThe public register of Telegram

Channel

Yandex for ML

@yandexforml

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

18,665subscribers

+22 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001433806174
TypeChannel
Username@yandexforml
Description💙Канал для ML-сообщества от Яндекса и место встречи специалистов в сфере анализа данных. Чат→ https://t.me/+XR1fd2QNnaIxNTUy Вопросы: @Ekaterina_Lyagina Все каналы по стекам: https://t.me/addlist/Hrq31w2p1vUyOGZi
CreatedBetween 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live11 August 2026
Measurements held6
Confirmed unchanged1 time, most recently 11 August 2026
On Telegramt.me/yandexforml

Growth

18,64318,66518,6546 August 2026 — 18,643 subscribers6 August 2026 — 18,645 subscribers9 August 2026 — 18,650 subscribers10 August 2026 — 18,648 subscribers11 August 2026 — 18,659 subscribers11 August 2026 — 18,665 subscribers6 August 202611 August 2026
6 measurements spanning 6 days, net +22. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 18,640–18,668 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
11 Aug 2026, 22:5718,665+6
11 Aug 2026, 02:2618,659+11
10 Aug 2026, 00:0218,648-2
9 Aug 2026, 01:1018,650+5
6 Aug 2026, 22:4018,645+2
6 Aug 2026, 09:0518,643first reading

Engagement

8 posts held, back to 30 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 17 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
14.5%
avg views ÷ 18,665 subscribers
Avg views / post
2,700
8 posts measured
Reaction rate
0.907%
reactions ÷ views · ER floor
Posts in window
8
of 8 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 12 August 2026
Posts held8 (30 July 202612 August 2026)
Views total21,610
Reactions total196
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken13 Aug 2026, 00:04 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Photos
1,250
Videos
82
Links
500

Lifetime counters from Telegram’s own channel header, read 13 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
23s
Average length
23s

Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no mark.

Reaction mix

196 reactions across 8 posts, in 10 distinct kinds. The most used accounts for 50.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
9950.5%
🔥4824.5%
👏199.69%
👍94.59%
❤‍🔥73.57%
👀52.55%
🥰42.04%
😁31.53%
🎉10.51%
🤩10.51%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 8 of the 8 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 196reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 8 most recent posts we hold, published 30 July 2026 to 12 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

12 Aug 2026, 13:04 UTC≈1,570 views18 reactionsread 13 August 2026
Photo

📖 Что читают в команде ML Лавки? В свободное время ML-разработчики читают статьи, каналы бизнес-юнитов Яндекса, книги по тайм-менеджменту и управлению креативными людьми и... бессмертную классику вроде «Анны Карениной»! 👷 В новом посте из рубрики «Обучено Яндекс Лавкой» собрали в карточках любимые книги или материалы, которые читают наши разработчики после работы. Рекомендациями поделились: ⚪️ Тимур Исхаков, ML-ра

9❤‍🔥4👀4👏1

11 Aug 2026, 11:21 UTC≈2,170 views17 reactionsread 13 August 2026
Photo

⭐️ Сегодня знакомим со спикерами от Яндекса 📆 Practical ML Conf 2026 пройдёт 19 сентября в Москве и онлайн. На конференции будем обсуждать практический ML, который реально влияет на бизнес. Приготовили для вас хардовые доклады, дискуссии и классные активности. Наши спикеры — независимые эксперты, специалисты из разных IT-компаний и приглашённые гости с keynote-докладами. Про всех будем рассказывать постепенно, за о

10❤‍🔥3🔥3😁1

8 Aug 2026, 10:02 UTC≈2,930 views13 reactionsread 13 August 2026
Photo

🔗 Автоматизируем базу знаний с AI, заглядываем за кулисы ICML и возвращаем пользователей Лавки с помощью uplift-моделей. Всё это и многое другое — в новом дайджесте Yandex for ML 🚕 Куда сходить ⚪️ Последний шанс зарегистрироваться на ML Global Recap’H1 2026, который пройдёт 13 августа. Обсудим итоги ICML и других международных конференций, главные ML-тренды первого полугодия 2026 года и собственный опыт команды. 🌎

9👏2🔥2

7 Aug 2026, 12:04 UTC≈2,660 views10 reactionsread 13 August 2026
Photo

🥹 Как выжать из железа максимум: эволюция инференса в Алисе Привет, меня зовут Витя Хаймоненко, я разработчик в команде голосовой активации Алисы. Сегодня я расскажу, как мы прошли путь от инференса на чистом C корутинами до современных движков и квантизованных моделей — и какой инженерный опыт из этого вынесли. 🅿️ Железо фиксировано, а модели растут На колонке одновременно крутится несколько нейросетей: ⚪️ Актив

7👀1👏1🔥1

6 Aug 2026, 12:04 UTC≈2,410 views30 reactionsread 13 August 2026
Photo

🔗 За кулисами реактивации Если пользователь давно ничего не заказывал, кажется, что решение простое: оценить вероятность оттока и предложить клиентам с самыми рискованными показателями скидку побольше. Но есть нюанс 🧐 Высокий риск означает, что человек, скорее всего, не сделает новый заказ. Он не говорит, поможет ли конкретный офер его вернуть. Именно для этого и нужны uplift-модели. 🅿️ Не вероятность покупки, а э

13🔥9👏6🤩1😁1

5 Aug 2026, 12:01 UTC≈2,670 views38 reactionsread 13 August 2026
Photo

🧿 Мы привезли графовую foundation-модель на воркшоп ICML 2026 Всем привет, это Людмила Прохоренкова, исследователь в Yandex Research. Наша команда занимается машинным обучением для задач, где важны связи между объектами: от академических исследований графовых моделей до их применения в продуктах Яндекса. Одна из важных частей нашей работы — проведение исследований и публикация результатов на ведущих ML-конференциях

17🔥13👍7👏1

31 Jul 2026, 12:00 UTC≈3,850 views29 reactionsread 13 August 2026
Photo

🔗 Грифон: как мы адаптируем LLM-принципы в рекомендательных системах Всем привет, это Дарья Тихонович из службы рекомендательных технологий в Яндекс R&D. Мы с командой разрабатываем новые семантические, генеративные и фундаментальные технологии для рекомендательных систем. 🔛 Одна из наших задач — адаптировать в рексистемах предсказательный принцип LLM. Только мы хотим предугадывать не слова, а айтемы и тренируем мо

13🔥11🥰4😁1

30 Jul 2026, 14:01 UTC≈3,350 views41 reactionsread 12 August 2026
Photo

📦 Предсказываем время сборки на складах в Лавке Привет! На связи Джавид Фаталиев, ML-разработчик в команде Яндекс Лавки. В рубрике «Обучено Яндекс Лавкой» мы рассказывали, как прогнозируем время доставки — интервал, который мы показываем пользователю на главной странице Лавки. 👳 А в этом посте мы поговорим про предсказание, которое остаётся незаметным для юзера, но влияет на скорость доставки, — время сборки на скл

21🔥9👏8👍2🎉1

Showing the 8 most recent of 8 posts we hold for @yandexforml. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Citation-graph rank

Citation-graph rank — 264,044 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 7 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Machinelearning
@ai_machinelearning_big_data · 286,250
Telegram ranks this channel #10 of 95 here — alongside 94 others — read 10 August 2026
XOR
@xor_journal · 158,763
Telegram ranks this channel #22 of 93 here — alongside 92 others — read 12 August 2026

This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 11 August 2026 — this entry's latest reading, not the date you are reading this.

“Yandex for ML” (@yandexforml), 18,665 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/yandexforml.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.