Deploying a Machine Learning Model to AWS SageMaker Complete Guide - PART 01
The article explains how to deploy a custom-made machine learning model or a model from Hugging Face on AWS using services like SageMaker, API Gateway, and Lambda Functions.
Why it matters
This guide helps developers understand how to leverage AWS's powerful AI/ML services to deploy custom models, making it accessible to other developers and applications.
Key Points
- 1Explains the key AWS services used for model deployment
- 2Walks through the process of creating a SageMaker notebook instance
- 3Discusses the importance of IAM roles and permissions for secure deployment
Details
The article provides a step-by-step guide on deploying a machine learning model on AWS. It introduces the key AWS services involved - SageMaker for model hosting, API Gateway for exposing the model via an API, and Lambda Functions for handling request routing and formatting. The author explains the process of creating a SageMaker notebook instance, which is a Jupyter Notebook running on an ML compute instance. The article also discusses the importance of IAM roles and permissions, which are used to grant the necessary access and capabilities to the various AWS services involved in the deployment.
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