Operations | Monitoring | ITSM | DevOps | Cloud

Modern DevOps teams that run dynamic, ephemeral environments (e.g., serverless) often struggle to keep up with the ever-increasing volume of logs, making it even more difficult to ensure that engineers can effectively troubleshoot incidents. During an incident, the trial-and-error process of finding and confirming which logs are relevant to your investigation can be time consuming and laborious. This results in employee frustration, degraded performance for customers, and lost revenue.

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).

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.

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.

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.