Building a Scalable ML Pipeline and API in AWS

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments.  This blog post discusses an end-to-end ML pipeline on AWS SageMaker that leverages serverless computing, event-trigger-based data processing, and external API integrations. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.