mlops
End-to-end MLOps guidance on AWS — platform selection, training, inference, pipelines, monitoring, and cost optimization. This skill should be used when the user asks to "build an ML pipeline", "deploy a model on SageMaker", "set up MLOps", "configure SageMaker Pipelines", "choose between SageMaker and Bedrock", "deploy ML models to production", "set up model monitoring", "use MLflow on AWS", "train a model with Spot instances", "configure inference endpoints", "set up distributed training", or mentions SageMaker, MLflow, Kubeflow, ML pipelines, model registry, model monitoring, hyperparameter tuning, inference endpoints, or MLOps on AWS.
Cloud & DevOps4 files