--Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer transformative potential to further enhance the intelligence of LAE services. However, deploying LAIMs in LAE poses several challenges, including the significant gap between their computational/storage demands and the limited onboard resources of LAE entities, the mismatch between lab-trained LAIMs and dynamic physical environments, and the inefficiencies of traditional decoupled designs for sensing, communication, and computation. T o address these issues, we first propose a hierarchical system architecture tailored for LAIM deployment and present representative LAE application scenarios. Next, we explore key enabling techniques that facilitate the mutual co-evolution of LAIMs and low-altitude systems, and introduce a task-oriented execution pipeline for scalable and adaptive service delivery. Then, the proposed framework is validated through real-world case studies. Finally, we outline open challenges to inspire future research. The low-altitude economy (LAE) is rapidly emerging as a critical engine of global industrial innovation and economic growth.

Serverless computing is a new type of cloud-based computation infrastructure initially developed for web microservices and IoT applications. As it frees model developers from concerns regarding capacity planning, configuration, management, maintenance, operating and scaling of containers, VMs and physical servers, serverless computing has gained popularity with machine learning (ML) researchers in recent years. Moreover, the benefits of serverless computing have also piqued interest in adopting it to data-intensive workloads such as ETL (extract, transform, load), query processing and ML, where it can provide significant cost reductions. Riding this trend, a research team from ETH Zürich and Microsoft recently conducted a systematic, comparative study of distributed ML training over serverless infrastructures (FaaS) and "serverful" infrastructures (IaaS), aiming to identify and understand the system tradeoffs involved in distributed ML training with serverless infrastructures. Serverless computing is offered by major cloud service providers such as AWS Lambda, Azure Functions and Google Cloud Functions.

Imagine you would like to train a deep learning model where you have thousands of images, but your system does not have any GPU. It would be hard to train large training models without GPU, so you will generally use google collab to train your model using google's GPU's. Consider your system memory is full, and you have important documents and videos to be stored and should be secured. Google drive can be one solution to store all your files, including documents, images, and videos up to 15GB, and offers security and back-up. Above mentioned scenarios are some of the applications of Cloud Computing, one of the advantages of using cloud computing is that you only pay for what we use.

How does Azure Cosmos DB work under the hood?

Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to its exposure to external adversaries, making it a lucrative attack vector for malicious actors. A datacenter infected with malware can cause data loss and/or major disruptions to service for its users. This paper analyzes and compares various Convolutional Neural Networks (CNNs) for online detection of malware in cloud IaaS. The detection is performed based on behavioural data using process level performance metrics including cpu usage, memory usage, disk usage etc. We have used the state of the art DenseNets and ResNets in effectively detecting malware in online cloud system. CNN are designed to extract features from data gathered from a live malware running on a real cloud environment. Experiments are performed on OpenStack (a cloud IaaS software) testbed designed to replicate a typical 3-tier web architecture. Comparative analysis is performed for different metrics for different CNN models used in this research.

Cybersecurity professionals have a hard job. Not only are they tasked with developing solutions to constantly changing risks, but they cannot know what those attacks will consist of until after they've already been launched. Though cybersecurity experts can certainly offer insights into what digital dangers may come next, these predictions are limited and make proactive solution development challenging. Luckily, by increasing the use of machine learning, cybersecurity groups are able to take advantage of advanced pattern recognition technologies to better determine what attacks are on the horizon. On the surface, it seems like cybersecurity professionals would be focused on designing stronger barriers to attack and establishing firmer encryption standards, but at its core, the field is driven by data.

From cut-throat competition, eyebrow-raising co-opetition, and major advances in cloud-based machine learning, 2017 was a pivotal - and productive - year for the cloud, setting the stage for what looks likely to be the most exciting year yet. The market swing is already in full force. Thanks to a full-fledged embrace by the enterprise, the cloud is undergoing dramatic transformation as vendors rush to meet the infrastructure and business needs of today's top companies. According to Gartner, the overall market likely grew by close to 20 percent in 2017, and IaaS in particular saw close to 40 percent growth. With digital transformation at the top of every executive's mind, it's likely that this trend will only accelerate.

Oracle is strengthening its cloud offerings to gain an upper hand over rivals and differentiate from the crowd. Thomas Kurian, president of product development at Oracle, told Gulf News that the company is applying artificial intelligence and machine learning to its cloud offerings in a bid to help customers lower cost, cut risk, accelerate innovation and get predictive insights. Moreover, he said that Oracle will be the only cloud provider to offer enterprise service-level of 99.95 per cent on infrastructure security. "If we don't meet that, we will take penalties. It is because of the design of what we do for reliability. Other vendors offer no guarantees around performance. Enterprise customers require critical business applications to perform at consistent and reliable levels," he said.

Even those new to IT have probably heard that everyone is "moving to the cloud." This transition from standard infrastructure is thanks in large part to Amazon Web Services. Currently, AWS offers "over 90 fully featured services for computing, storage, networking, analytics, application services, deployment, identity and access management, directory services, security. All of these services offer powerful, cloud-based, pay-as-you-go alternatives compared to their legacy counterparts." To help you better understand the scale at which AWS is capable of running, keep in mind that there are currently over 1 million enterprise customers worldwide who run the AWS marketplace software 70 million hours per month, according to DMR. Likewise, as of 2017, AWS owned 34% of all cloud (IaaS, PaaS) while the next three competitors Microsoft, Google, and IBM have 11%, 8%, 6% respectively, according to Synergy Group.

The digital world is constantly changing--a good thing, especially because new advancements and tools are what propel businesses and entire industries (like healthcare[Office1]) forward. But in order to successfully integrate technology into business operations, you can't only look at what's happening in the world of technology today, you've got to look at what's coming next as well. In this vein, my partner, Daniel Newman, recently brought you five predictions on how blockchain will drive digital transformation and discussed his AI and automation predictions for the future. Today, I'm here to share how 2018 will be the year of AI and insights as a service (IaaS). What can you expect, and what can you do to stay ahead of the curve?