Progressive Delivery in Kubernetes

Can your Kubernetes deployments automatically detect failures and roll back? Learn how progressive delivery provides an automated safety net for every release.

#1about 3 minutes

Understanding the core concepts of progressive delivery

Progressive delivery is a set of deployment techniques that avoid all-or-nothing releases by gradually rolling out changes to a subset of users.

#2about 1 minute

The business case for progressive delivery

Adopting progressive delivery helps avoid downtime, limit the blast radius of failures, and shorten the time from idea to production by providing safety guardrails.

#3about 5 minutes

Exploring key progressive delivery techniques

An overview of common deployment strategies including default Kubernetes rolling updates, blue-green deployments, canary releases, and feature flags.

#4about 1 minute

Why monitoring is the new form of testing

Effective monitoring and observability are crucial for progressive delivery, allowing teams to automatically detect issues in production and react quickly.

#5about 2 minutes

Implementing progressive delivery in Kubernetes

Kubernetes services and ingress controllers are the foundational components that enable traffic shifting for strategies like canary deployments.

#6about 3 minutes

Using Argo Rollouts for advanced Kubernetes deployments

Argo Rollouts is an open-source Kubernetes controller that provides advanced deployment capabilities like canary releases and blue-green deployments out of the box.

#7about 7 minutes

Demo of a successful canary deployment with Argo Rollouts

A step-by-step demonstration shows how Argo Rollouts manages a successful canary release, gradually shifting traffic from a blue to a green version.

#8about 4 minutes

Demo of an automatic rollback after a failed deployment

See how Argo Rollouts automatically detects a failing canary deployment and rolls back to the stable version without manual intervention.

#9about 2 minutes

Enhancing rollouts with AI-powered log analysis

AI tools like Google Gemini can be integrated into the analysis step to interpret complex logs, determine deployment health, and generate clear summaries for rollback decisions.