Which AWS service can help automate the deployment of ML models?

AWS Lambda is a serverless compute service that allows you to run code in response to events and automatically manage the compute resources required. This makes it particularly useful for automating tasks related to the deployment of machine learning (ML) models. With Lambda, you can trigger functions that load and serve ML models, respond to incoming requests, and even scale automatically based on demand without having to manage the underlying infrastructure.

In the context of deploying ML models, you can integrate AWS Lambda with other services such as Amazon API Gateway to create an API that serves your model predictions. Additionally, AWS Lambda can interact with other AWS services, such as S3 (for model storage) or DynamoDB (for storing inference results), making it an effective solution for end-to-end automation in deploying and managing ML models.

The other services listed play distinct roles and are not primarily focused on automating ML model deployments. For instance, while AWS EC2 provides scalable compute resources, it requires more management and setup effort than Lambda, particularly for event-driven scenarios. AWS S3 is primarily for storage and does not directly automate ML deployment, although it is used to store model artifacts. AWS RDS is a managed relational database service, which is also not relevant to the deployment of ML models