Tag: Architecture

Feed: Featured Blog Posts - Data Science Central. Author: Vimal N Suba. Depending on what news headline you have read, you may have perceived an Artificial Intelligence (AI) system as either an Alexa or Siri assistant that understands all your commands, a deep learning system that can recognize dog or a cat from image, a system that recommends personalized medicine, or an intelligent, overpowering machine that can overtake all human tasks and render humans useless. Few of these definitions can be termed as visionary, few fear mongering and rest of them being evolutionary. Last month, I was at Artificial Intelligence (AI) Summit 2018 in San ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . ColumnStore is a columnar storage engine for MariaDB designed for big data, linear scaleability, and performance. It is included in the MariaDB AX distribution. ColumnStore is composed of User Modules that handle queries and Performance Modules that process, store and retrieve data. It can be installed on a single node or distributed across multiple nodes. Before attempting to install ColumnStore, you should first consider the architecture of your deployment. How many User Modules (UM) do you require? How many Performance Modules (PM) do you require? How much disk space do you require? ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . When you are deploying MariaDB ColumnStore on SUSE Linux Enterprise Server, you can download the installation media from MariaDB and install the packages on each host in your deployment. In installing from a download, you can run the install either as the root or a non-root user. Package Dependencies Unlike the package manager installation, installing ColumnStore from a binary tar file does not automatically retrieve package dependencies from the system or MariaDB repositories. As such, you need to manually call zypper to install the package dependencies on each host. Boost Libraries ColumnStore ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . In cases where you are deploying MariaDB ColumnStore on Debian or Ubuntu operating systems, you can download the installation media from MariaDB and install the tar package on each host in your deployment. When install from a tar download, you can perform the installation either as the root user or a non-root user. Package Dependencies Unlike the package manager installation, installing ColumnStore from a binary tar file does not automatically retrieve package dependencies from the system or MariaDB repositories. As such, you need to manually call apt-get to install the package dependencies ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . In situations where you are deploying MariaDB ColumnStore on CentOS or Red Hat Enterprise Linux, you can download the installation media from MariaDB and install the packages on each host in your deployment. When installing from a download, you can run the install either as the root or a non-root user. Package Dependencies Unlike the package manager installation, installing ColumnStore from a binary tar file does not automatically retrieve package dependencies from the system or MariaDB repositories. As such, you need to manually call yum to install the package dependencies on each ... Read More

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. By Janos Szendi-Varga, Senior Consultant at GraphAware | October 18, 2018Reading time: 5 minutes Do you think there is no space for a graph database in your company? Or it would be a huge effort to integrate a graph database into your product? I have to tell you: You can use a graph database like Neo4j without touching your product, and you can use it for managing your company’s knowledge as well as to improve your software development process. So, even if your business problem is not inherently graphy (hard to believe ... Read More

Feed: AWS Compute Blog. Author: tiffany jernigan (@tiffanyfayj). → Part 2: Amazon EFS Post by: Jeremy Cowan, Ronnie Eichler, and Tiffany Jernigan Introduction Containers are emerging as the default compute primitive for building cloud-native applications.  They facilitate the adoption of continuous delivery, and help increase infrastructure use. However, deploying stateful application as containers has been challenging because containers have short life-spans, get re-deployed frequently, are scaled up and down dynamically, and often share the same host with other containers. All of these factors make it challenging for you to appropriately align the lifecycles of storage volumes and containers. Before Docker ... Read More

Feed: AWS Compute Blog. Author: Christie Gifrin. This post is courtesy of Maxime Thomas, DevOps Partner Solutions Architect – AWSThis story begins when I joined AWS at the beginning of the year. I had a hard time during my ramp-up period trying to handle the amount of information coming from all directions. Technical training, meetings, new colleagues, in a worldwide company—the volume of information was overwhelming. However, my first priority was to get my AWS Certified Solutions Architect — Professional certification. This gave me plenty of opportunities to learn and focus on all of the new domains I had never ... Read More

Feed: Tractica. Author: Aditya Kaul. Huawei announced its AI strategy at the Huawei Connect 2018 conference in Shanghai. Tractica was invited to the conference with AI being a central theme. In the past, Huawei Connect has been focused on its enterprise cloud services business, with AI being mentioned on the sidelines. This year, Huawei had “Activate Intelligence” as the conference tagline, with more than 25,000 people in attendance filling up Shanghai’s Expo and Convention Center. Huawei’s Full-Stack AI: Chips, Frameworks, and the Cloud Huawei’s Chairman and Rotating CEO, Eric Xu, gave the keynote speech announcing the company’s AI strategy covering ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Muhammad Rizwan. Yann LeCun, Leon Bottou, Yosuha Bengio and Patrick Haffner proposed a neural network architecture for handwritten and machine-printed character recognition in 1990’s which they called LeNet-5. The architecture is straightforward and simple to understand that’s why it is mostly used as a first step for teaching Convolutional Neural Network. LeNet-5 Architecture Original image published in [LeCun et al., 1998]The LeNet-5 architecture consists of two sets of convolutional and average pooling layers, followed by a flattening convolutional layer, then two fully-connected layers and finally a softmax classifier. First Layer: The input ... Read More