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
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Telegram — AI Machine Learning Big Data
Read full article at Telegram — AI Machine Learning Big DataWhen 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...
AI Summary & Key Takeaways
- 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...
When one pipeline solves too many problems
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