Most machine learning courses teach you to train a model and call it done. This course starts where those leave off. You will build a complete, production-grade MLOps system on AWS from scratch — a self-improving recycling image classifier that trains, deploys, monitors, and retrains itself automatically. Every component is real, every line of code runs in your AWS account, and by the end you have a working closed-loop pipeline that would hold up in a professional environment.
The course opens with a thorough introduction to Amazon Bedrock — model providers, the Bedrock playground, inference profiles, guardrails, and LLM inference configuration — so you understand the foundation before using Claude as an auto-labeling engine inside the retraining loop.
From there you fine-tune a Vision Transformer on SageMaker, deploy it to a live real-time endpoint with data capture enabled, and build a CloudWatch dashboard that tracks model confidence and retraining activity in real time. You then wire up an API Gateway and Lambda function that serves predictions to the outside world, detects low-confidence outputs, and automatically saves those weak cases to S3 for review.
