Какие знания по ML устаревают быстрее всего в 2026

Machine learning is changing faster than you might think: approaches that were recently considered standard are already losing relevance. In this video, Alexander Dubeykovsky (Avito ML Engineer, former Yandex, MLinside expert) examines which ML knowledge will become obsolete the fastest in 2026: from classic algorithms and older NLP approaches to tools and libraries that are gradually being phased out of production. At the end of the video, we'll discuss tools and libraries that can already be considered obsolete, along with practical guidelines for focusing on in your education and career. Who will find this video useful and why? • Newcomers to ML — to avoid wasting time on outdated approaches and immediately learn the latest stack • Junior ML engineers — to understand what knowledge is truly in demand in practice • Those preparing for interviews — to avoid focusing on topics that are rarely asked or used • Data scientists — to reevaluate their stack and update their skills • Backend/Data engineers transitioning to ML — to quickly understand how the industry has changed • Practicing specialists — to keep up with trends and understand where the market is heading AI and Data Analysis Specialization Course: https://mlinside.ru/specializaciya/?u... Follow MLinside on Telegram: https://t.me/+xPCRRLylQh5lMmI6 Timecodes: 0:00 – Introduction 0:53 – Classical Algorithms 2:29 – Statistical Methods 3:58 – LLM 5:57 – Bonus: Legacy Tools and Libraries 7:54 – A Word from Viktor Kantor 8:19 – Conclusion #machinelearning #ML #DataScience #linearalgebra #SVD #NLP #recommendersystems #LLM #LoRA #artificialintelligence #AI #modeltraining