Book Image

IoT Edge Computing with MicroK8s

By : Karthikeyan Shanmugam
Book Image

IoT Edge Computing with MicroK8s

By: Karthikeyan Shanmugam

Overview of this book

Are you facing challenges with developing, deploying, monitoring, clustering, storing, securing, and managing Kubernetes in production environments as you're not familiar with infrastructure technologies? MicroK8s - a zero-ops, lightweight, and CNCF-compliant Kubernetes with a small footprint is the apt solution for you. This book gets you up and running with production-grade, highly available (HA) Kubernetes clusters on MicroK8s using best practices and examples based on IoT and edge computing. Beginning with an introduction to Kubernetes, MicroK8s, and IoT and edge computing architectures, this book shows you how to install, deploy sample apps, and enable add-ons (like DNS and dashboard) on the MicroK8s platform. You’ll work with multi-node Kubernetes clusters on Raspberry Pi and networking plugins (such as Calico and Cilium) and implement service mesh, load balancing with MetalLB and Ingress, and AI/ML workloads on MicroK8s. You’ll also understand how to secure containers, monitor infrastructure and apps with Prometheus, Grafana, and the ELK stack, manage storage replication with OpenEBS, resist component failure using a HA cluster, and more, as well as take a sneak peek into future trends. By the end of this book, you’ll be able to use MicroK8 to build and implement scenarios for IoT and edge computing workloads in a production environment.
Table of Contents (24 chapters)
1
Part 1: Foundations of Kubernetes and MicroK8s
4
Part 2: Kubernetes as the Preferred Platform for IOT and Edge Computing
7
Part 3: Running Applications on MicroK8s
14
Part 4: Deploying and Managing Applications on MicroK8s
21
Frequently Asked Questions About MicroK8s

Overview of the ML workflow

Kubeflow aims to be your Kubernetes ML toolkit. The ML tools that are required for your workflow can then be specified using the Kubeflow configurations and the workflow can be deployed to various platforms for testing and production use as required.

Let’s have a look at the Kubeflow components before we get into the intricacies of ML workflows.

Introduction – Kubeflow and its components

Kubeflow is a system for deploying, scaling, and managing complex systems based on Kubernetes. For data scientists, Kubeflow is the go-to platform for building and testing ML pipelines. It is also for ML developers and operations teams who wish to deploy ML systems in a variety of contexts for development, testing, and production.

Kubeflow is a framework for establishing the components of your ML system on top of Kubernetes, as shown in the following diagram:

Figure 9.1 – Kubeflow components on top of Kubernetes

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