Data scientists often train their models locally and look for a proper hosting service to deploy their models. Unfortunately, there’s no one set mechanism or guide to deploying pre-trained models to the cloud. In this post, we look at deploying trained models to Amazon SageMaker hosting to reduce your deployment time.
SageMaker is a fully managed machine learning (ML) service. With SageMaker, you can quickly build and train ML models and directly deploy them into a production-ready hosted environment. Additionally, you don’t need to manage servers. You get an integrated Jupyter notebook environment with easy access to your data sources. You can perform data analysis, train your models, and test them using your own algorithms or use SageMaker-provided ML algorithms that are optimized to run efficiently against large datasets spread across multiple machines. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.

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