This article presents a comprehensive MLOps workflow for batch inference using Amazon SageMaker, EventBridge, Lambda, Terraform, and GitLab CI/CD.

• Automates model training, monitoring, retraining, and registration with error handling
• Multi-account strategy: model development in central account, inference in staging/production
• Training pipeline runs on schedule or S3 trigger, registers models exceeding performance thresholds
• Batch inference pipeline automatically uses latest approved model from registry
• Data quality checks via SageMaker Model Monitor; model quality via custom processing steps
• Training with HPO triggered when model quality check fails or manually by data scientist
• Manual approval required for HPO-trained models; automatic for recalibrated models
• Infrastructure as Code using Terraform for reproducible, version-controlled deployments
• Sample code provided uses single account, single GitLab pipeline, S3 event triggers
• Three SageMaker pipelines: training, batch inference, training with HPO

This solution reduces operational complexity and costs by automating ML lifecycle management, monitoring, and infrastructure provisioning for production batch inference workloads.