Deploying machine learning algorithms for real-time inference is of utmost importance to power customer-facing web applications and other use cases. One of the prerequisites for a functional real-time ML serving architecture is to containerize the applications. Containerizing the runtimes provides a reproducible environment to train and deploy the ML models. In this article, we’ll look at some best practices and the process of deploying machine learning models using custom containers for real-time inference.
Vertex AI: Serving architecture for real-time machine learning
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