This guide provides an introduction to kubectl, the command-line tool used to communicate with the Kubernetes API. It covers command syntax, useful commands, flags, and tips and tricks. It also discusses the ecosystem of plugins and tools built to expand the functionalities of kubectl and Kubernetes.
Eric Larssen discusses the shortcomings of Terraform in infrastructure as code management, such as bespoke patterns, drift management issues, HCL language complexity, and licensing drama, and suggests the Kubernetes operator pattern as an alternative.
In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
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