Mobile network operators (MNOs) manage Radio Access Networks (RANs) with massive amounts of cells over multiple radio generations (2G-5G). To handle such complexity, operations teams rely on monitoring systems, including anomaly detection tools that identify unexpected behaviors. In this paper, we present c-ANEMON, a Contextual ANomaly dEtection MONitor for the RAN based on Graph Neural Networks (GNNs). Our solution captures spatio-temporal variations by analyzing the behavior of individual cells in relation to their local neighborhoods, enabling the detection of anomalies that are independent of external mobility factors. This, in turn, allows focusing on anomalies associated with network issues (e.g., misconfigurations, equipment failures). We evaluate c-ANEMON using real-world data from a large European metropolitan area (7,890 cells; 3 months). First, we show that the GNN model within our solution generalizes effectively to cells from previously unseen areas, suggesting the possibility of using a single model across extensive deployment regions. Then, we analyze the anomalies detected by c-ANEMON through manual inspection and define several categories of long-lasting anomalies (6+ hours). Notably, 45.95% of these anomalies fall into a category that is more likely to require intervention by operations teams.

The interest in exploring planetary bodies for scientific investigation and in-situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. Additionally, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and post-mission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in-situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25 degrees, loose soil, and unstructured terrain, highlighting their advantages compared to wheeled rover systems. We successfully verified the approach in analog deployments at the BeyondGravity ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach.

With the public release of ChatGPT and Microsoft's $10-billion investment into OpenAI, artificial intelligence (AI) is quickly gaining mainstream acceptance. For enterprise networking professionals, this means there is a very real possibility that AI traffic will affect their networks in major ways, both positive and negative. As AI becomes a core feature in mission-critical software, how should network teams and networking professionals adjust to stay ahead of the trend? Andrew Coward, GM of Software Defined Networking at IBM, argues that the enterprise has already lost control of its networks. The shift to the cloud has left the traditional enterprise network stranded, and AI and automation are required if enterprises hope to regain control.

Machine learning is a subfield of artificial intelligence that involves building algorithms that can automatically learn and improve from data without being explicitly programmed. Machine learning has a wide range of applications in areas such as image and speech recognition, natural language processing, and predictive modeling. In a machine learning project, a model is trained using a labeled dataset, and then the model is used to make predictions or decisions on new, unseen data. There are several steps involved in a typical machine learning project, including initiating the project, identifying business goals, framing the machine learning problem, analyzing the data, designing the model, processing the data, developing the model, deploying the model, testing the model, and deploying to production. Each of these steps is important in ensuring that the model is able to deliver value and achieve the desired outcomes.

Now more than ever, every company is a data company. By 2025, individuals and companies around the world will produce an estimated 463 exabytes of data each day, 1 1. Jeff Desjardins, "How much data is generated each day?" World Economic Forum, April 17, 2019. With that in mind, most businesses have begun to address the operational aspects of data management--for instance, determining how to build and maintain a data lake or how to integrate data scientists and other technology experts into existing teams. Fewer companies have systematically considered and started to address the ethical aspects of data management, which could have broad ramifications and responsibilities. If algorithms are trained with biased data sets or data sets are breached, sold without consent, or otherwise mishandled, for instance, companies can incur significant reputational and financial costs. Board members could even be held personally liable.

Over the past decade, network operations teams have had to deal with a number of issues in their networks--from increased complexity to more distributed environments. With AIOps, you can start optimizing your networks now and prepare for the future. AIOps lets you manage your network like never before. According to Gartner, AIOps combines big data and machine learning to automate IT operations processes such as event correlation, anomaly detection, and causality determination to name a few. It can be defined as the application of machine learning (ML) and data science to IT operations problems.

When discussing AI in software development, we often talk about machine learning. But is this the same thing? Can machine learning replace DevOps? And can AI completely replace DevOps? This article will explore the differences between machine learning and AI and how to integrate both in your organization.

AI has been heralded as the new "brains" for software applications, a role long held by databases. Unfortunately, AI is not so easy for application developers and operations teams to adopt and absorb. Actually, incorporating machine-learning models (which power AI) in productivity-focused applications -- to make them smarter -- is overly difficult and complex. Moreover, ML models depend on specific combinations of hardware and software infrastructure. Without the right infrastructure, the models either cannot perform well enough to be viable or, in some cases, become prohibitively costly.

Launched in 2015, Shopee is the leading e-commerce platform in Southeast Asia and Taiwan serving millions of users everyday. We provide consumers an easy, secure, fast, and enjoyable online shopping experience. Come and make history with us! https://careers.shopee.pl/ The Operation teams at Shopee covers the operational end-to-end process, from when the buyer searches for a product listed on the Shopee platform, to the moment the buyer receives the products. The team analyses and monitors operational KPIs and conducts root cause analysis when operation performance fluctuates.

Software development teams are adapting AI & ML models into their apps and platforms to lessen DevOps lags. AI-driven DevOps will be the way of the future and flow with the tide. Software development tool vendors are speeding up the pace of integrating AI and machine learning models into their apps while seeking ways to lessen the delays in DevOps teams. Artificial intelligence will replace people as the essential tool for computing & analysis, revolutionizing how teams create, distribute, deploy, and manage applications since humans are not suited to handle the enormous volumes of data and computing required in daily operations. But first, let's grasp how AI and DevOps are related before we explore how ai ml will impact DevOps.