Part 1: Introduction to observing machine learning workloads on Amazon EKS

This article is the first part of a four-part series about observing machine learning (ML) workloads on Amazon EKS, focusing on the unique challenges and monitoring requirements of MLOps.

• MLOps aims to streamline deployment, observability, and maintenance of ML models in production
• Key challenges include model drift, resource management, data quality, and versioning
• Observability is critical for gaining insights into ML model and infrastructure performance
• Essential metrics to monitor include:
  • Resource usage (CPU, memory, GPU)
  • Latency and throughput
  • Model performance metrics
  • Data quality and drift
  • Error rates and failures
• Multiple personas are involved in MLOps monitoring, including business stakeholders, data engineers, data scientists, and DevOps teams

The series will provide step-by-step guidance on end-to-end observability for ML infrastructure on Amazon EKS, helping organizations maintain and improve their ML models' performance and effectiveness.