Operations | Monitoring | ITSM | DevOps | Cloud

With the surge in digitization, the concept of cloud migration has become a fundamental reality for many organizations. VMware, a widely accepted leader in server virtualization, has enabled many businesses to take advantage of virtual machines’ benefits. However, as businesses evolve, so do their requirements. This often leads to the need for moving these VMware-based virtual machines to more flexible and scalable platforms like Microsoft Azure.

In this post we will explain how to use OpenShift monitoring (alert manager) to monitor NeuVector using NeuVector prometheus-exporter NeuVector has published a Prometheus exporter which we will use in the post.

Building full-stack applications can be challenging, especially when developing the backend and frontend at the same time. In this scenario, frontend teams may have to wait for the backend team to finish building an API before they implement. This is where Mirage.js comes in. In this tutorial, you will explore how to use Mirage.js in frontend applications and mock backend requests for services that have not yet been developed.

Over the past five years, enterprise networking has undergone a significant transformation driven by advancements in technology, the rise of cloud and SaaS applications, the decentralization of the workforce, and the need for agility, scalability, and cost mitigation. These factors have led organizations to shift from on-premise network management systems (NMS) to cloud-managed networking platforms and to adopt technologies like Software-Defined Wide Area Networking (SD-WAN).

Azure Event Grid domain is a fully managed event routing service that simplifies the development of event-based applications by providing a centralized event routing service for cloud-based applications and services.

Every team has guardrails, whether you recognize them or not. They’re a form of automation that can have significant impact on your software development process and the people doing the work. They’re another way to give toil the boot and keep developers in the flow. We’ve made the case for engineering automation in a previous article; here’s how guardrails as automations ensure that agreed upon boundaries and ways of working are codified into team processes.

Data scientists and machine learning engineers are often looking for tools that could ease their work. Kubeflow and MLFlow are two of the most popular open-source tools in the machine learning operations (MLOps) space. They are often considered when kickstarting a new AI/ML initiative, so comparisons between them are not surprising. This blog covers a very controversial topic, answering a question that many people from the industry have: Kubeflow vs MLFlow: Which one is better?