- Model Function
Explore the process of deploying machine learning models as serverless AWS Lambda functions. Learn to package required Python libraries and the model into a zip file, upload it to S3, and configure Lambda runtime settings. Understand how to test functions and include dependencies via Lambda layers for scalable model serving.
In order to author a Lambda function that uses libraries outside the base Python distribution, you’ll need to set up a local environment that defines the function and includes all of the dependencies. Once your function is defined, you can upload the function by creating a zip file of the local environment, uploading the resulting file to S3, and configuring a Lambda function from the file uploaded to S3.
Getting started
The first step in this process is to create a directory with all of the dependencies installed locally.
While it’s possible to perform this process on a local machine, we will perform these tasks in our code widget below to provide a clean Python environment.
Creating the environment
The next step is to install the libraries needed for ...