Operations | Monitoring | ITSM | DevOps | Cloud

Cloud native is a term that’s been around for many years but really started gaining traction in 2015 and 2016. This could be attributed to the rise of Docker, which was released a few years prior. Still, many organizations started becoming more aware of the benefits of running their workloads in the cloud. Whether because of cost savings or ease of operations, companies were increasingly looking into whether they should be getting on this “cloud native” trend.

Cloud costs are a daily concern for companies running applications on Amazon Web Services (AWS). This makes sense because many organizations struggle with unexpected AWS charges. Although many organizations have a cloud budget in place, accurately controlling costs is difficult. Real-time cost remediation is often difficult for many, leading to inflated AWS costs every billing cycle. No matter where you are on your cloud journey, reducing AWS costs is something you’ll want to do continuously.

MLOps pipelines are a set of steps that automate the process of creating and maintaining AI/ML models. In other words, Data Scientists create multiple notebooks while building their experiments, and naturally the next step is a transition from experiments to production-ready code. The best way to do this is to build an effective MLOps pipeline. What’s the alternative, I hear you ask? Well, each time you want to create a model, you run your notebooks manually.

For many organisations, multi-cloud has become or is becoming inevitable. After all, it’s unlikely there is a single cloud out there that is able to support all your requirements. More than likely however, is the chance that your business is becoming multi-cloud by stealth. Organisations typically use several, to dozens, to hundreds of Software-as-a-Service (SaaS) products, as well as a handful of Infrastructure-as-a-Service (IaaS) hosting services, and development Platforms-as-a-Service (PaaS).

Welcome once again to another Logic App Best practices, Tips, and Tricks. In my previous blog posts, I talked about some of the most essential best practices you should have while working with the Azure Logic App: And some tips and tricks: Today I’m going to speak about in my opinion one of the most annoying features/tasks and recommend some Best practices, Tips, and Tricks to minimize it: working with expressions.

In the first part of my DevOps 101 series, we tackled the fundamentals of What, who why and how. In this follow-up, I’m going to focus on how to gain buy-in across your organization. That said, who do you need buy-in from? DevOps is the union of people, processes and products to enable continuous delivery of value to our end users, according to Microsoft’s Donovan Brown. That’s a good explanation, because the focus is not on tooling, or processes, or even just people.

In 2020, we started an internal project to price our data center systems to understand what they might cost to run in the AWS or Azure cloud environments. We initially used some of the online pricing tools available from these vendors, but they were slow and difficult to use in bulk. You can find the public cloud tools here: AWS and Azure. We were forced to add individual systems one at a time and pick options for each because of the nature of these tools.