We will explore the role of Kubernetes in modern software design, and learn about its architecture.

Modern Software Architecture, Containerization, and Kubernetes

Using "kubectl"

Kubernetes version

Containerized Applications

Docker and Kubernetes

Using A First Manifest

Docker vs Kubernetes

Kubernetes Architecture

Kubernetes Infrastructure

Do You Know Kubernetes Architecture?

Introduction to Kubernetes (K8s)

We learn how to deploy software using Kuberentes, and will use "kubectl" extensively. 

Deploying a First (Stateless) Application

Stateless Apps on Kubernetes

A Kubernetes Deployment

Scaling and Monitoring an Application

Deploying and Maintaining Software on Kubernetes

Scaling and Monitoring a Deployment

Deploying, Scaling, and Monitoring a Stateful Application

Deploying and Scaling Stateful Apps 1

Deploying and Scaling Stateful Apps 2

Stateful Apps on Kubernetes

Deploying, Scaling and Monitoring Kubernetes Storage

Do You Know Kubernetes Storage?

Pods With Attached Storage

Monitoring Pods and Storage

StatefulSet With Attached Storage

Deploying Software on K8s

Building upon what we learned in the previous chapters, we will understand how Kubernetes can support data pipelines and MLOps.

Networking, Load Balancing, and Security

Options

True

False

Networking, Load balancing, and Ingress on Kubernetes

Creating a Service

Deploy a Service

Data Pipelines on Kubernetes

ETL and ELT data pipelines

Deploy an ETL pipeline on Kubernetes

MLOps on Kubernetes

MLOps with Kubernetes

An MLOps Workflow

Wrap-Up

Data Engineering and MLOps

Kubernetes is a solution that orchestrates and maintains containerized applications effectively. Since its introduction in 2014 by Google, this open-source system has developed into the tool of choice for managing all kinds of distributed workloads. It is an essential part of today's DevOps world, helping to develop and deploy scalable applications and workflows of any kind.

In this interactive course, you'll become a Kubernetes champ with hands-on experience using Kubernetes on the command line. You will master kubectl, the Kubernetes command line tool, and use it to interact with real Kubernetes clusters. You will learn the fundamentals of Kubernetes and understand how to deploy and orchestrate containers using Manifests and kubectl instructions.

Kubernetes is a solution that orchestrates and maintains containerized applications effectively. Since its introduction in 2014 by Google, this open-source system has developed into the tool of choice for managing all kinds of distributed workloads. It is an essential part of today's DevOps world, helping to develop and deploy scalable applications and workflows of any kind.

In this course, you'll become a Kubernetes champ with hands-on experience using Kubernetes on the command line. You will master kubectl, the Kubernetes command line tool, and use it to interact with real Kubernetes clusters. You will learn the fundamentals of Kubernetes and understand how to deploy and orchestrate containers using Manifests and kubectl instructions.

After an introduction to Kubernetes and its relation to modern
software architecture and cloud-nativeness, you will learn your first Kubernetes objects and how to apply them like a pro.

As you progress, you'll learn about more advanced concepts like stateful applications, storage, and networking. To wrap up, you will use these concepts in an exemplary MLOps workflow.

Introduction to Docker

In this course, you will learn the fundamentals of Kubernetes and deploy and orchestrate containers using Manifests and kubectl instructions.

Introduction to Kubernetes

Containerization and Virtualization with Docker and Kubernetes

Professional Data Engineer in Python

MLOps is a set of best practices for deploying and maintaining ML models that can be implemented easily with the objects that Kubernetes offers. 

MLOps relies on isoloated experimental "playgrounds" to test models. Kubernetes supports this with objects like Pods and Persistent Storage.

What makes Kubernetes great to build scalable systems is the reproducabilitly when using Pods, Deployments, StatefulSets & Kubernetes storage. 

Kubeflow, a well-known framework for MLOps, is a system that covers only some parts of the ML life cycle.

Kubernetes is a great tool for MLOps, as it will build and package your ML workflows automatically.

MLOps with Kubernetes

Introduction to Kubernetes

Hands-on interactive exercise