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.

Many model serving and deployment workflows have repeatable, boilerplate aspects which you can automate using modern DevOps techniques like high frequency deployment and microservices architectures. This approach can enable the ML engineers to focus on the model instead of the surrounding code and infrastructure.