5 Cloud Computing Predictions that will Transform 2019DATAQUEST

March 13, 2019 | 166 views

We are in the early stages of what Gartner calls the second decade of cloud computing. Over the last decade, cloud adoption has increased across the industry. Cloud is being adopted for the benefits like instantaneous availability of compute resources, scalability, and pay-as-you-go. Cloud platforms help organizations to move faster towards their business goals. The complexity of managing vendor relationships with datacenters, hardware vendors moves over to the public cloud operator.  That provides the flexibility to move forward, faster in an uncertain business environment. Business project iterations have a much faster turnaround.

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Thompson Technologies

Thompson provides exceptional IT talent to clients across diverse industries on a contract, contract-to-hire and direct hire basis. The company excels at filling challenging positions and identifying qualified candidates for its clients’ technical environments. A certified Veteran Owned Small Business (VOSB), Thompson has been a consecutive winner of Best & Brightest Companies to Work For (Atlanta and National competitions) as well as Inavero’s Best of Staffing, Talent.

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SOFTWARE

AI's Impact on Improving Customer Experience

Article | July 8, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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FUTURE TECH

The Evolution of Quantum Computing and What its Future Beholds

Article | July 14, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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AI TECH

Language Models: Emerging Types and Why They Matter

Article | July 20, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

Thompson Technologies

Thompson provides exceptional IT talent to clients across diverse industries on a contract, contract-to-hire and direct hire basis. The company excels at filling challenging positions and identifying qualified candidates for its clients’ technical environments. A certified Veteran Owned Small Business (VOSB), Thompson has been a consecutive winner of Best & Brightest Companies to Work For (Atlanta and National competitions) as well as Inavero’s Best of Staffing, Talent.

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VMware Updates PKS to Advance Enterprise Kubernetes

eWeek | April 23, 2019

VMware is including the Pod Security Policy capability, which is still considered to be a beta feature in the open-source Kubernetes cloud-native container orchestration project, as a supported component in its enterprise-grade Kubernetes platform. VMware announced version 1.4 of Enterprise PKS on April 23, bringing new functionality to help organizations operationalize the cloud-native Kubernetes container orchestration platform. PKS, which is an acronym for the Pivotal Container Service, is a joint product effort from VMware and its partner Pivotal, integrating Kubernetes with components from Pivotal as well as VMware. The PKS 1.4 update is based on the Kubernetes 1.13 release and integrates new security and automation capabilities, as well as the inclusion of VMware's NSX-T 2.4 virtual networking technology. "NSX-T 2.4 is included with a VMware Enterprise PKS license," Scott Buchanan, senior director of the Cloud Native Apps Business Unit at VMware, told eWEEK. "Part of the value of VMware Enterprise PKS is that the components—including NSX-T—are integrated, validated and more readily deployed by the customer." PKS was launched in August 2017 and has received multiple incremental updates over the past year and a half. The previous release was PKS 1.3,which was announced on Jan. 16, integrating support for Kubernetes 1.12.

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VMware Patches DoS, Information Disclosure Flaws in Graphics Components

SecurityWeek | April 12, 2019

Patches released this week by VMware for its ESXi, Workstation and Fusion products address “important” denial-of-service (DoS) and information disclosure vulnerabilities affecting graphics components. One of the flaws, tracked as CVE-2019-5516, has been described by VMware as an out-of-bounds read bug in the vertex shader functionality. Exploitation of the flaw requires authentication and it can lead to information disclosure or a DoS condition on the virtual machine (VM). The vulnerability, reported to VMware by Piotr Bania of Cisco Talos, can only be exploited if the 3D acceleration feature is enabled on the VM. This feature is enabled by default on Fusion and Workstation, but not on ESXi. A researcher known as RanchoIce, of Tencent Security ZhanluLab, also found some out-of-bounds read vulnerabilities in a graphics component, specifically the shader translator. Exploitation of the flaw, identified as CVE-2019-5517, can also result in information disclosure and a DoS condition. The last security hole, CVE-2019-5520, is also caused by an out-of-bounds read bug in a graphics component, but it appears that it can only be exploited for information disclosure. This issue was reported to VMware by a researcher who uses the online moniker instructorthrough Trend Micro's Zero Day Initiative (ZDI).

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VMware Refreshes Cloud Management Platform

SDxCentral | April 02, 2019

VMware updated its vRealize cloud management platform with capacity and cost management capabilities and multi-cloud comparison tools, among other new features. Specifically, the vendor announced four new product releases: vRealize Operations 7.5, vRealize Network Insight 4.1, vRealize Automation 7.6, and vRealize Suite Lifecycle Manager 2.1. All of these are slated for availability in VMware’s first quarter fiscal year 2020. The new releases come on the heels of VMware’s (latest) cloud push last month that included more VMware Cloud on AWS regions, expanded multi-cloud management tools, and a new hyperconverged infrastructure (HCI) device jointly engineered with Dell EMC. The goal with these updates is to “help our customers holistically manage data centers and clouds in a way where you get this management control plane that hides all the operational complexity and breaks down operational silos, both on premises and across clouds,” said Taruna Gandhi, director of product marketing for VMware’s Cloud Management business unit. Self-Driving Operations: vRealize Operations aims to enable “self-driving operations,” so that customers can take a hands-off approach to their production operations — across applications, infrastructure, and clouds. And this includes four tenants: continuous performance optimization, efficient capacity management, intelligent remediation, and integrated compliance, Gandhi said. “We continue our investment along these four value pillars,” she added.

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VMware Updates PKS to Advance Enterprise Kubernetes

eWeek | April 23, 2019

VMware is including the Pod Security Policy capability, which is still considered to be a beta feature in the open-source Kubernetes cloud-native container orchestration project, as a supported component in its enterprise-grade Kubernetes platform. VMware announced version 1.4 of Enterprise PKS on April 23, bringing new functionality to help organizations operationalize the cloud-native Kubernetes container orchestration platform. PKS, which is an acronym for the Pivotal Container Service, is a joint product effort from VMware and its partner Pivotal, integrating Kubernetes with components from Pivotal as well as VMware. The PKS 1.4 update is based on the Kubernetes 1.13 release and integrates new security and automation capabilities, as well as the inclusion of VMware's NSX-T 2.4 virtual networking technology. "NSX-T 2.4 is included with a VMware Enterprise PKS license," Scott Buchanan, senior director of the Cloud Native Apps Business Unit at VMware, told eWEEK. "Part of the value of VMware Enterprise PKS is that the components—including NSX-T—are integrated, validated and more readily deployed by the customer." PKS was launched in August 2017 and has received multiple incremental updates over the past year and a half. The previous release was PKS 1.3,which was announced on Jan. 16, integrating support for Kubernetes 1.12.

Read More

VMware Patches DoS, Information Disclosure Flaws in Graphics Components

SecurityWeek | April 12, 2019

Patches released this week by VMware for its ESXi, Workstation and Fusion products address “important” denial-of-service (DoS) and information disclosure vulnerabilities affecting graphics components. One of the flaws, tracked as CVE-2019-5516, has been described by VMware as an out-of-bounds read bug in the vertex shader functionality. Exploitation of the flaw requires authentication and it can lead to information disclosure or a DoS condition on the virtual machine (VM). The vulnerability, reported to VMware by Piotr Bania of Cisco Talos, can only be exploited if the 3D acceleration feature is enabled on the VM. This feature is enabled by default on Fusion and Workstation, but not on ESXi. A researcher known as RanchoIce, of Tencent Security ZhanluLab, also found some out-of-bounds read vulnerabilities in a graphics component, specifically the shader translator. Exploitation of the flaw, identified as CVE-2019-5517, can also result in information disclosure and a DoS condition. The last security hole, CVE-2019-5520, is also caused by an out-of-bounds read bug in a graphics component, but it appears that it can only be exploited for information disclosure. This issue was reported to VMware by a researcher who uses the online moniker instructorthrough Trend Micro's Zero Day Initiative (ZDI).

Read More

VMware Refreshes Cloud Management Platform

SDxCentral | April 02, 2019

VMware updated its vRealize cloud management platform with capacity and cost management capabilities and multi-cloud comparison tools, among other new features. Specifically, the vendor announced four new product releases: vRealize Operations 7.5, vRealize Network Insight 4.1, vRealize Automation 7.6, and vRealize Suite Lifecycle Manager 2.1. All of these are slated for availability in VMware’s first quarter fiscal year 2020. The new releases come on the heels of VMware’s (latest) cloud push last month that included more VMware Cloud on AWS regions, expanded multi-cloud management tools, and a new hyperconverged infrastructure (HCI) device jointly engineered with Dell EMC. The goal with these updates is to “help our customers holistically manage data centers and clouds in a way where you get this management control plane that hides all the operational complexity and breaks down operational silos, both on premises and across clouds,” said Taruna Gandhi, director of product marketing for VMware’s Cloud Management business unit. Self-Driving Operations: vRealize Operations aims to enable “self-driving operations,” so that customers can take a hands-off approach to their production operations — across applications, infrastructure, and clouds. And this includes four tenants: continuous performance optimization, efficient capacity management, intelligent remediation, and integrated compliance, Gandhi said. “We continue our investment along these four value pillars,” she added.

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