Challenges with Machine Learning in production
One of the hardest parts of Machine Learning (ML) is deploying and operating ML models
in production applications. These challenges fall maily into the following categories: model
deployment and serving, model monitoring, and model governance.
Challenges with model deployment and serving After models are trained and ready to deploy in a production environment, lack of consistency with model deployment and serving workflows can present challenges in terms of scaling your model deployments to meet the increasing numbers of ML usecases across your business. Challenges with model monitoring Machine Learning (ML) models predict the world around them which is constantly changing. The unique and complex nature of model behavior and model lifecycle present challenges after the models are deployed. Challenges with model governance Businesses implement ML models across their entire organization, spanning a large spectrum of usecases. When you start deploying more than just a couple models in production, a lot of complex governance and management challenges arise.