Operations | Monitoring | ITSM | DevOps | Cloud

Designing a production service environment around Apache Kafka that delivers low latency and zero-data loss at scale is non-trivial. Indeed, it’s the holy grail of messaging systems. In this blog post, I’ll outline some of the fundamental service design considerations that you’ll need to take into account in order to get your service architecture to measure up. Let’s start with the basics.

15 November 2022: Canonical announced today the availability of new enterprise-grade Ubuntu images designed for next-gen Intel IoT platforms. Purpose-built for industrial environments and use cases, the latest Ubuntu images on Intel hardware deliver the performance, safety, and end-to-end security enterprises expect from the most widely used Operating System (OS) among professional developers with latest Intel technologies pre-enabled and available.

The use of cloud computing by financial institutions has significantly increased in the last few years, a trend that was further accelerated by the COVID-19 pandemic. In the next few years, financial institutions will need to continuously balance the pressure to innovate quickly while managing risk and combating financial crime.

RAN has incrementally evolved with every generation of mobile telecommunications, thus enabling faster data transfers between user devices and core networks. The amount of data has increased more than ever with an increase in the number of interlinked devices. With existing network architectures, challenges lie in handling increasing workloads with the ability to process, analyse and transfer data faster. The 5G ecosystem requires virtual implementations of RAN.

So, you’ve decided to use Azure as your primary cloud platform and want to calculate your infrastructure costs. You estimate them based on listed prices, and rest assured that your startup/project will meet its budget. And then, suddenly, at the end of the month, you receive an invoice from Azure for an amount two times higher than you originally expected.

Looking at the report that Gartner did in 2022 regarding top technology trends, AI engineering represents an important pillar in the near future. It is composed of three core technologies: DataOps, MLOps and DevOps.The discipline’s main purpose is to develop AI models that can quickly and continuously provide business value. For instance, models that enable cross-functional collaboration, automation, data analysis, and machine learning.

On 8 November 2022, at Open Source Experience Paris, Canonical announced that Charmed Kubeflow, Canonical’s enterprise-ready Kubeflow distribution, now integrates with MindSpore, a deep learning framework open-sourced by Huawei. Charmed Kubeflow is an end-to-end MLOps platform with optimised complex model training capabilities designed for use with Kubernetes.

Welcome to the concluding blog of this mini-series on tapping into the fourth industrial revolution. In Part I, we introduced and assessed the current status in the IT and OT domains. In Part II, we discussed the automation pyramid of modern factories and the need to adopt a more holistic approach toward closing the divide.

If you’re embarking on a new project and evaluating public clouds, the cost involved will be one of your main considerations. You might decide to use Google Cloud Platform (GCP) as your primary cloud platform. If so, you’ll want to estimate costs based on listed prices. But it might surprise you to find a higher bill than you originally expected.