[D] What are people using for orchestration of frequent model re-training and deployment?

My team have a few models in production where model re-training happens on a pretty ad-hoc basis every few weeks. We run training, track using MLFlow (not using MLFlow's model registry), then if we want to deploy the new model, we manually update the model tag in the application repo, create the new application image, downloading the new model artifacts from s3, then deploy.

This has worked pretty well for us. The problem is, we have a project coming up …

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