Tutorial: Creating a Machine Learning API in AWS (2024)

Creating an ML API with AWS provides a scalable and reliable platform for deploying and managing your machine learning models. AWS offers several services that can be utilized to build ML APIs, and in this tutorial, we will explore two popular methods: AWS Lambda and Amazon SageMaker.

Method 1: AWS Lambda

Step 1: Model Deployment

First, you need to train your machine learning model using your preferred framework (e.g., TensorFlow, PyTorch, scikit-learn) and save it in a serialized format (e.g., pickle, HDF5). Ensure that the model is ready for inference.

Step 2: Create an AWS Lambda Function

1. Go to the AWS Management Console and navigate to AWS Lambda.
2. Click “Create function” and choose a suitable name for your function.
3. Select the “Author from scratch” option.
4. Choose the desired runtime environment (e.g., Python).
5. In the “Function code” section, upload your trained model file and write the necessary code to load and invoke the model for inference.
6. Configure the function with an appropriate execution role and set memory and timeout values based on your requirements.
7. Save and deploy the…