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.
OpenSearch is an open-source search and analytics suite. Developers build solutions for search, data observability, data ingestion and more using OpenSearch. Another popular use case is log analytics. You take the logs from applications, servers and network elements, feed them into OpenSearch, and use the rich search and visualisation functionality to identify issues.
This is the third blog from a series focusing on how public clouds meet telecommunication operators’ business demands. In the previous two blogs, we talked about how Amazon Web Services (AWS) and Google Cloud Platform have enabled telcos to run critical workloads on public clouds. In this last part of our series, you’ll hear about Microsoft Azure cloud and why it’s a trusted platform for the telecommunication industry to host their workloads.
In the first part of this blog series, we discussed the run-time (in)security challenge, which can leave your code and data vulnerable to attacks by both the privileged system software of the public cloud infrastructure, as well as its administrators. We also introduced the concept of trusted execution environments and confidential computing, (CC), as a paradigm to address this challenge.
Welcome to Part II of this three-part mini-series on bridging the gap between operational technology (OT) and information technology (IT) in Industry 4.0. In Part I, we set the stage for the remainder of the series and gave an overview of IT and OT, the two technological layers of modern industrial factories. In this blog, we expand on that knowledge by confronting the two domains and discussing the automation pyramid concept.
You launch a startup or a new project in your organisation. You decide to use Amazon Web Services (AWS) as your primary cloud platform. You estimate costs 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 AWS for an amount two times higher than you originally expected.
