Model deployment

As we conclude our series on model deployment patterns, this final post explores one of the most widely used approaches: dynamic deployment on Google Kubernetes Engine (GKE). Building on the simplicity of static deployment and the versatility of dynamic deployment across platforms, this post takes a hands-on approach. You’ll implement dynamic deployment step by step…

Continuing our series on model deployment patterns, the previous post covered dynamic deployment and its various implementations—whether on a virtual machine, in a container, or using a serverless approach. In this post, we’ll explore a practical example of dynamic deployment, examining each component and reviewing the results. There are several approaches to building a dynamic…

In the second deployment pattern, we’ll understand the most commonly used pattern: dynamic deployment. In dynamic deployment, the model is clearly separated from the other components of the application. This means the model can be updated without updating the entire application. This approach allows for better separation of concerns and provides more control to the…

To provide business value and achieve real-world benefits, a crucial aspect of delivering a machine learning system is model deployment. Deploying a model involves making it available to accept queries from users of the production system [1]. Models are developed in a research environment and deployed in a production environment through the model deployment stage….