AWS SageMaker MLOps: Deploying ML Models to Production on AWS

SageMaker is AWS's ML platform, from training to deployment. MLOps pipelines, Model Registry, inference endpoints, drift monitoring: the complete guide for production.

Why SageMaker for MLOps?

Machine learning in production goes far beyond training a model in a notebook. You need to manage data pipelines, version models, automate training, deploy with availability SLAs, and monitor data and performance drift. SageMaker addresses this entire lifecycle with native AWS integration.

Key SageMaker MLOps Components

SageMaker Pipelines: ML workflow orchestration (preprocessing, training, evaluation, deployment)
Model Registry: model versioning and governance, with manual approval before deployment
Feature Store: feature storage and reuse across teams and models
Model Monitor: data and performance drift monitoring in production
Clarify: bias detection and model explainability

Inference Endpoints

Real-time Endpoint: <100ms latency, auto-scaling, ideal for synchronous ML APIs
Serverless Inference: scale-to-zero, per-request billing, variable latency (cold start) — for intermittent loads
Async Inference: for long inferences (>1 min), results in S3
Batch Transform: batch scoring on S3 datasets, no permanent endpoint

Model Monitor: Detecting Drift

A production model inevitably degrades over time — input data distribution changes (data drift), relationships between features and target evolve (concept drift). Model Monitor automates this surveillance, comparing production feature distributions to the training baseline using KL-divergence and chi-square tests. Violations generate CloudWatch alerts that can trigger automatic retraining via EventBridge → Lambda → Pipeline.

Cost Optimisation

Use Spot instances for training (up to 90% cheaper) — SageMaker handles checkpoints and automatic resumption
Prefer Serverless Inference for low-traffic models (<1,000 req/day)
SageMaker Savings Plans for permanent production endpoints

Conclusion

SageMaker provides a complete MLOps platform, from data ingestion to production monitoring. The key is to adopt a pipeline approach from the start — no manually trained notebook models without traceability. Move2Cloud supports data teams in setting up MLOps pipelines on AWS.