Production ML on AWS: Monitoring, Troubleshooting, and Cost Optimization

Why It Matters

These operational and cost best practices help teams move models from development to reliable, scalable production endpoints while controlling run costs and detecting model degradation early, reducing downtime and unexpected cloud spend. Adopting structured monitoring and automation accelerates production readiness and ongoing maintenance for ML systems.

Summary

The video demonstrates how to monitor, troubleshoot, and optimize production ML deployments on AWS, using CloudWatch logs to validate API Gateway and Lambda-based serverless inference pipelines. It walks through triggering an API, inspecting log groups and log streams to confirm successful invocations or diagnose errors, and recommends structured JSON logging and key metrics to collect (latency, invocation count, errors, model performance metrics such as accuracy/precision/recall). The presenter outlines monitoring tools including CloudWatch metrics, SageMaker Model Monitor, and alarms for infrastructure health across Lambda, ECS, SageMaker and EKS. It also covers cost-optimization tactics—right-sizing compute, using serverless or Lambda container images, stopping endpoints when idle, leveraging spot instances for non-time-critical training, S3 storage classes, and AWS recommendation tools like Compute Optimizer.

Original Description

Your machine learning model is live—now how do you keep it running efficiently and affordably? In this final installment of our AWS deployment series, we move beyond the "launch" and focus on MLOps lifecycle management.

Learn how to verify your setup using Amazon CloudWatch, interpret log streams to troubleshoot errors, and implement industry-standard best practices for monitoring and cost control.

What we cover in this video:

- Log Verification: Step-by-step guide to matching API Gateway IDs with CloudWatch Log Groups.

- Live Troubleshooting: Triggering the API via curl and analyzing log streams for success (200 OK) vs. failure.

- Model Monitoring Best Practices: Why "Application Health" isn't enough—how to track Model Drift, data quality, and prediction accuracy.

- Infrastructure Health: Setting thresholds and alarms for Lambda latency and resource utilization.

Cost Optimization for ML:

- When to use Serverless (Lambda) vs. SageMaker.

- The power of Spot Instances for non-critical training.

- Using AWS Compute Optimizer and S3 Storage Classes to reduce overhead.

- Key Takeaways: Summarizing the journey from a local Python notebook to a scalable, secure, and monitored production API.

Master these tools to ensure your data science projects are not just functional, but enterprise-ready.