What are the key considerations when deploying Machine Learning models in production?

Key things to focus on when deploying Machine Learning models into production include model performance, scalability, monitoring, and security. You want to make sure that the model works on the real world’s data. One needs to scale up—this refers to flexing your model’s muscles upon changes in load.

The monitoring should ideally be continuous because it can help problems be detected, among them model drift, which makes the models’ performance degrade over time. The security issue assures that major importance lies in keeping both the model and data processed by the model well protected against unauthorized access and any breach in data.