Introduction to ML on Kubernetes: Data Preparation, Model Training & Model Serving

Kubernetes isn’t just for microservices 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.

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.