Public clouds enabled digital transformation at unprecedented speed. But their operational costs over time can be exacting as compute needs increase. Hybrid clouds emerged as an alternative to gain the benefits of both worlds: private infrastructure that allows for lower operational expenditures and tighter control, and public clouds that can scale with ease.
What are you tinkering with over your vacation? Perhaps you have ambitions to have a beautiful homelab, launch pet projects on a public cloud, or build do it yourself home automation. Ubuntu has broad community and commercial support, making it the logical choice when you need to provision machines and containers with a mix of proprietary and open source software. Once your homelab, home automation, or public cloud projects are running, you can deploy Landscape beta.
As we enter the holiday season, online shopping and payment systems are gearing up for higher traffic and workloads. Ensuring that these applications can handle the increased demand without slowing down or crashing is critical for providing a smooth and efficient experience for customers. One way to improve the performance and reliability of these applications is by using chiselled Ubuntu images in your containerised deployment.
If you are looking for a small-scale cloud deployment suitable for the edge, chances are you came across Canonical’s Micro cloud solution. These are small-footprint clusters of compute nodes with distributed storage and secure networking, optimised for repeatable and reliable remote deployments.
Repatriation in cloud computing refers to moving workloads from the public cloud to on-premise infrastructure. Sarah Wang and Martin Casado from Andreessen Horowitz have written one of the most popular articles about repatriation: they explain the motivation with the significant cost savings possible. For software-based businesses, public cloud spend can rise to 50% of the cost of revenue (COR). Reducing these costs has the potential for significant margin increases.
MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. MLOps is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring.
Financial Institutions (FIs) need to respond with agility and business velocity to keep pace with changing economic conditions. Yet, emerging competition from fintechs and challenger banks and increasing customer expectations is making this task difficult, especially as regulatory and compliance requirements increase. Embracing the next phase of digital transformation is an imperative for financial institutions to sustain and grow in a competitive environment of rising cost pressures.
Large enterprises usually have more than 1,000 systems running. Even smaller organisations may have hundreds of applications in their public cloud spaces or on their servers. In this world of IT systems, application migrations are common for the following reasons: In addition to these three general motivations, there is a growing repatriation trend. The public cloud provides the optimal environment for most systems but not for all. For some systems, private cloud hosting can be more cost-efficient.
Enterprises are looking to capitalise on the new wave of small form-factor computing and navigate the shift to the edge. Device manufacturers across the world are racing to build embedded, connected devices that will deliver on the promise of the fourth industrial revolution. Many of them are looking to explore data-driven value-chain optimisations, predictive maintenance and or new digital customer experiences.
AI/ML is reinventing the reality of many industries, including retail. From brick-and-mortar stores to online marketplaces, retail companies are all increasing their investments in artificial intelligence, in order to gain a competitive advantage, better understand their customers and solve some of their long-lasting problems.
