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

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.