Scholarlink

We have all been there: your machine learning model performs perfectly fine in offline testing on all accuracy and performance parameters. But when this model is deployed into production, suddenly the performance and accuracy nosedives. This new model starts affecting the performance of the overall application, its predictive power over the period of time decreases, and the model fails to handle real-world scenarios. This is where machine learning projects face their biggest challenges: the stage after deployment.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Prototype to Production: Scaling ML at Speed and Scale

  • 章节

  • 发表日期：

    2026年

查看全文
  • Anshuman Srivastava,
  • Abhinav Garg,
  • Anshuman Mishra

摘要

We have all been there: your machine learning model performs perfectly fine in offline testing on all accuracy and performance parameters. But when this model is deployed into production, suddenly the performance and accuracy nosedives. This new model starts affecting the performance of the overall application, its predictive power over the period of time decreases, and the model fails to handle real-world scenarios. This is where machine learning projects face their biggest challenges: the stage after deployment.