Operations | Monitoring | ITSM | DevOps | Cloud

In this post, we will walk through how to connect GitLab’s Auto DevOps feature with a Rancher-managed Kubernetes cluster, making use of a feature introduced in Rancher v2.2.0 called Authorized Cluster Endpoint. Readers can expect to walk away with an understanding of how GitLab integrates with Kubernetes and how Rancher simplifies this workflow with Authorized Cluster Endpoint.

Service level objectives are an important tool for maintaining application performance, ensuring a consistent customer experience, and setting expectations about service performance for both internal and external users. We are very pleased to announce the availability of a new monitor uptime and SLO widget that makes it simple to monitor the status of your SLOs and communicate that status to your teams, executives, or external customers.

Containers have rapidly become a necessary part of the modern data center. Containers can be built on any of a number of operating system foundations — so how do you choose one over another?

AWS introduced Lambda Layers at re:invent 2018 as a way to share code and data between functions within and across different accounts. It’s a useful tool and something many AWS customers have been asking for. However, since we already have numerous ways of sharing code, including package managers such as NPM, when should we use Layers instead? In this post, we will look at how Lambda Layers works, the problem it solves and the new challenges it introduces.

Stackery is a tool to deploy complete serverless applications via Amazon Web Services (AWS). Epsagon monitors and tracks your serverless components to increase observability. Here’s how they can not only work together but improve each other. Let’s start with a scenario: it’s late in the day on Thursday, traffic to your site is way up, and you have reports of problems.

We had a blast at this year’s SREcon in Brooklyn, NY. The three-day event included meeting with integration partners and deepening our understanding of website reliability from a DevOps perspective.

With over 48,000 stars on GitHub, more than 75,000 commits, and with major contributors like Google and Red Hat, Kubernetes has rapidly taken over the container ecosystem to become the true leader of container orchestration platforms. Kubernetes offers great features like rolling and rollback of deployments, container health checks, automatic container recovery, container auto-scaling based on metrics, service load balancing, service discovery (great for microservice architectures), and more.

This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn’t tried it out yet. To that end, I use Google Cloud for every step of this process.