When one pipeline solves too many problems
When one pipeline solves too many problems The more problems one pipeline solves, the more settings become cluttered, experiments compete for shared slots, and the contribution of specific mechanics becomes more difficult to evaluate. AI VK engineers began to use streams - separate pipelines...
- 01.When one pipeline solves too many problems The more problems one pipeline solves, the more settings become cluttered, experiments compete for shared slots, and the contribution of specific mechanics becomes more difficult to evaluate
- 02.AI VK engineers began to use streams - separate pipelines...
Когда один пайплайн решает слишком много задач Чем больше задач решает один пайплайн, тем больше настройки обрастают условиями, эксперименты конкурируют за общие слоты, а вклад конкретной механики труднее оценить. Инженеры AI VK стали использовать стримы — отдельные пайплайны под конкретные задачи, собранные из общих компонентов. Один запрос проходит через несколько стримов, а их результаты объединяются в одну выдачу. В статье разбираем, как стримы упрощают разработку и эксперименты, и как оптимизации помогают эффективнее использовать ресурсы. Читать на Хабре
Related Intelligence
All stories →OMRON to acquire OnLogic to address edge AI applications
OnLogic will keep its Vermont headquarters, as well as its global offices, as part of OMRON. Source: OnLogic As manufacturers increasingly use data and AI at the edge, they need reliable computing that works alongside real-time automation and control. OMRON Corp. this month said…
At New York Climate Week 2026, MIT connects with a global network
Each year, Climate Week NYC brings together more than 100,000 people in more than 1,000 events across the city to discuss climate challenges, explore solutions, and spur action. Participants come from industry, academia, philanthropy, nonprofits, government, and finance and…
Researchers find six patterns that make AI text so exhausting to read
A study from New York University and the University of Texas at Austin shows that frontier language models systematically overuse linguistic patterns that are hard for humans to parse. The researchers trace the problem to training on code and math, where correctness gets…