Welcome to
the Harness Blog.

Our latest thinking on Continuous Delivery, Customer Success, and what it will take for your company to soar to new heights.

continuous delivery blog - harness ci cd best practices - captain canary

Metrics to Improve Continuous Integration Performance

By Tiffany Jachja / March 17, 2021
Blog / Continuous Integration / Featured

It is important to determine which continuous integration metrics are best to measure in order to track performance. Learn which performance metrics Harness measures.

Argo Flux vs Harness – Filming My Journey

Around KubeCon North America 2019, two open source projects with […]

By Ravi Lachhman / March 25, 2020
Continuous Delivery / DevOps / Featured

Harness Product Update | March 2020

Are you as excited as I am about March updates? […]

By Omed Habib / March 23, 2020
Featured / Product Updates

Business Continuity with your Software Delivery Pipelines

All of us are on this ship together. For regulated organizations, a Business Continuity Plan is legally required but for those who are not can start.

By Ravi Lachhman / March 20, 2020
Blog / Change Management / Continuous Delivery / Featured

Metrics Based Process Mapping: How to improve your Software Delivery

Let’s discuss how to improve your software delivery with a practice known as Metrics-Based Process Mapping.

By Tiffany Jachja / March 19, 2020
Accelerate Metrics / Change Management / DevOps / Featured

Jenkins X vs Harness – Filming my Journey

Oh ship another comparison! Jenkins X vs Harness. You don’t have to be a GitOps expert to leverage Harness. Don’t fight the double learning curve.

By Ravi Lachhman / March 18, 2020
Blog / Continuous Delivery / DevOps / Featured / Jenkins Struggles / Self-Service

Everything You Need to Know About Verified GitOps

GitOps: A good start, but how can we make it better? Enter Verified GitOps. Verification in GitOps can be a challenge come and learn from Harness!

By Bryan Feuling / March 13, 2020
Blog / Continuous Delivery / DevOps / Featured / GitOps

Continuous Delivery for Machine Learning Systems

To enable data scientists, data engineers, and ML engineers to scale their processes around data management, model training, and deployment and operationalization.