Microsoft is looking beyond flash storage and hard drives to handle the seemingly unstoppable demand for cloud storage. Yesterday at its Ignite conference, the company announced Project HSD (via ZDNet), a new research initiative that's exploring how holographic storage could eventually be used for the cloud. And while it may sound far-fetched, the notion of holographic storage has been around since the 1960's. But now, Microsoft thinks it may be possible to use the medium effectively thanks to the rise of smartphone cameras. As the video above explains, holographic storage works by writing and reading data from an optical crystal.
Malaysian operator Maxis has announced it is working with Google Cloud to integrate data analytics into its business, from consumers to enterprise, network, retail channels and employees. The company's digital analytics transformation programme entails transitioning 100 percent of its business intelligence, data analytics and machine learning on-premise workloads to the cloud. Maxis has also established its Big Data and Advanced Analytics and AI Center of Excellence with data scientists and commitment programmes. Maxis is leveraging Artificial Intelligence and Machine Learning (AI/ML) services from Google Cloud, as well as from Google Cloud partners' technology solutions. Google Cloud and Maxis also plan to jointly develop a curated career development programme to build technical knowledge and in-house expertise, and grow the number of Google Certified Data Engineers within the organisation.
Are you looking for the Best Edureka Courses 2020? Edureka is an online technical training platform that offers Big Data, cloud computing, artificial intelligence, and blockchain-based courses. The classes can be attended to at any place and any time as per your choice Use our Android and iOS App to learn on the go. Their engaging learning platform, expert industry practitioners, and support ninjas make sure that you complete the course. Get lifetime accesses to the entire content including quizzes and assignments as the technology upgrades your content gets updated at no cost? Choose from a number of batches as per your convenience if you got something urgent to do, reschedule your batch for a later time. If you want to get started with top Edureka free courses check out the Edureka course catalog from the Edureka site. You will get tons of free courses online Edureka on the Edureka platform.
In our previous blog, we looked at how public clouds have set the pace and standards for satisfying the technology needs of data scientists, and how on-premises offerings have become increasingly attractive due to innovations such as Kubernetes and Kubeflow. Nevertheless, delivery of ML platforms on-premises is still not easy. The effort to replicate a public cloud ML experience requires enthusiasm and persistence in the face of potential frustration. To address this challenge, the Cisco community has developed an open source tool named MLAnywhere to assist IT teams in learning and mastering the new technology stacks that ML projects require. MLAnywhere provides an actual, usable outcome in the form of a deployed Kubeflow workflow (pipeline) with sample ML applications on top of Kubernetes via a clean and intuitive interface.
Thanks to the SAP devX external conferences program I had the opportunity to attend virtually the BlackHat USA conference. Together with DefCon, BlackHat is one the most advanced and exciting conference on offensive and defensive security that a security expert should attend. As explained in my previous article about BlackHat Europe 2019 the conference is proposing three main tracks: Briefings (advanced hacking and research presentations), Arsenal (live demo of open source security tools) and Sponsored sessions (presentation made by security companies). I made my selection among these different tracks according to my interest topics (Social Engineering, Machine Learning Security, Threat Intelligence, Code scanning, vulnerability management, ..) and here is a non exhaustive list from my personal selection of interesting topics: MLaaS (Machine Learning as a Service) platforms, are cloud based commercial services proposing pre-trained machine learning models and prediction functionalities deployed in a powerful cloud computing infrastructure and accessible to any user through an API. The goal is to benefit from a descent computing power hosted by a cloud provider but also take benefit from pre-trained models to make directly some predictions on the data.
Arm's acquisition by Nvidia has been rumored for a while, and now, it has been officially confirmed. This is a significant and well-tuned move for both sides. One that has been long-time coming, in fact. We review the steps leading to this outcome, and what this means for the AI chip market. This is the second high-profile acquisition for Nvidia in 2020, following the acquisition of Mellanox in April.
"Our research is investigating a training algorithm to obtain a global model as if it is trained by aggregating data in a single server, even when the data are placed in distributed servers, such as in edge computing," according to the statement. NTT's proposed technology has enabled developers to successfully train a global model in early experiments-even in cases where different types of data are used and the communication between servers is "asynchronous," meaning that each compute node's results are not dependent on receiving data and results from another node. NTT notes that interest in edge computing is growing because of the benefits for lower application latency, and expects that there will be community interest in the application of its research to edge compute and networking services. The company said it will continue to develop the technology for commercial applications, and will release the source code to promote collaboration.
The cloud computing race in 2020 will have a definite multi-cloud spin. Here's a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves. Google Cloud is filling out its executive ranks as it focuses its industries including supply chain, logistics, transportation, financial services, and healthcare. Google Cloud's plan to date is to leverage its machine learning, artificial intelligence, and analytics know-how into industries that are deploying multi-cloud architectures. For instance, Google Cloud is looking to stake out turf in retail since AWS competes with that customer base.
In this tutorial, we present the high-level steps that are involved in connecting an Android device to the cloud and developing analytics models to analyze sensor data. By the end of this tutorial you should be able to set up your own IoT hub for streaming, storing and processing device data. The following figure shows the architecture of our sample app. This tutorial requires an Android device (smartphone), an internet connection, and an IBM Cloud account. In Step 1 you will create an account on IBM Cloud and install an application on your Android phone.
In recent years, large organizations have committed billions to AI/Machine Learning (AI/ML) investment. According to CIO Magazine, the retail and banking sectors estimated that their 2019 spend on AI/ML would be, cumulatively, in excess of $11.6 Billion. The Healthcare sector was estimating an investment of approximately $36 Billion by 2025. Even with these huge financial commitments, some analysts predict that 87% of AI/ML Projects will fail to deliver as promised or never make it into production. Of particular note is that the vast majority of AI/ML projects today are targeted for internal datacenter deployment.