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:
- Best practices for optimizing how data science workloads run on Kubernetes
- The best open-source systems for monitoring Kubernetes clusters
- Specific areas where Kubernetes falls short for AI, and how you can fix these issues
