The Challenges of Running ML on Kubernetes & How AI SRE Can Help

Kubernetes isn’t just for containers anymore. As AI adoption accelerates, tools like Kubeflow and Argo Workflows have transformed Kubernetes into a powerful platform for running AI/ML workloads. But managing the complexities of data preparation, model training, and serving models requires the right approach—and tools.

What you’ll learn:

• Optimize Data Pipelines: How to prepare and process data for AI models using tools like Apache Airflow and Argo Workflows, with comparisons of their pros and cons in different scenarios.
• Train Models at Scale: Discover how platforms like Kubeflow, MLflow, and KAITO can be leveraged to orchestrate and scale AI model training, utilizing K8s’ native scalability to handle resource-intensive tasks like GPU utilization.
• Serve AI Models: Explore how K8s simplifies model serving and deployment with tools like Hugging Face and BentoML’s OpenLLM, providing insights into scaling, managing, and monitoring these services in production environments.
• Overcome Challenges of Running AI Workloads on K8s: Most common blindspots in running AI workloads on K8s and how to avoid them.
• How to leverage Komodor’s Klaudia agentic AI to troubleshoot and optimize AI/ML workflows.

Whether you’re just starting with ML on Kubernetes or looking to optimize your AI workflows, this eBook provides actionable insights to help you deploy and scale with confidence.