Machine learning in production challenges and tips
Machine learning in production challenges and tips Bringing a model from a notebook to a live service is hard. Data shifts, user behavior changes, and limited resources create real risks. The goal is to keep good results while the world around the model keeps changing. Clear goals, good monitoring, and simple processes help teams stay in control. Common production challenges include data drift, model performance decay, and a growing gap between research work and daily operations. If monitoring is weak or alerts are noisy, small issues become outages or costly mistakes. Latency and costs can also block real-time use. Finally, governance and reproducibility matter: easy to reproduce experiments and roll back when needed. ...