Introduction to Server-less and AWS Lambda
This article discusses how AWS Lambda can be used to deploy machine learning models, including the benefits of using Lambda functions such as infrastructure abstraction, cost-efficiency, and free tier usage.
Why it matters
AWS Lambda provides a serverless approach to deploying and running machine learning models, offering benefits like cost savings and reduced infrastructure management.
Key Points
- 1AWS Lambda enables the deployment of various applications, including machine learning models
- 2Lambda functions provide infrastructure abstraction, cost-efficiency, and free tier usage
- 3Creating a simple Lambda function is straightforward, with the function handling event data and context information
- 4The article mentions adjusting the Lambda function code to accommodate a test event with a URL parameter
Details
The article introduces AWS Lambda, a service that allows developers to run code without worrying about server management. For machine learning use cases, Lambda can be used to deploy models, such as sending a URL of a picture of pants to the deployed model and receiving the predicted classes and scores. The key benefits of using Lambda functions are infrastructure abstraction (no need to manage EC2 instances), cost-efficiency (pay-per-request model), and free tier usage (1 million free requests per month). The article walks through the process of creating a simple Lambda function, including understanding the event and context parameters passed to the function. It also shows how to adjust the function code to handle a test event with a URL parameter, which could be used to pass the input data to a machine learning model deployed on Lambda.
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