Kubernetes for MLOps Engineers

According to a recent Gartner report,* by 2023, more than 70% of global organizations will be running more than two containerized applications in production.

With adoption of Kubernetes as the container orchestration standard growing just as rapidly, it is essential for anyone in a Machine Learning Engineering role to be fully versed in Kubernetes architecture, monitoring, scheduling and troubleshooting.

In this guide, you’ll discover:

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In This Guide

New Monitoring Challenges

Challenges engineers face with capturing real-time performance metrics in Kubernetes.


Scale-Out vs. Scale-Up Architecture

Kubernetes is built with scale-out architecture. See what that means for AI/ML workloads.

Batch & Gang Scheduling

Why you should implement these capabilities to maximise your compute resources.