Acumos An Open Source AI Machine Learning Platform

March 14, 2018 | 72 views

Acumos is an Open Source Platform, which supports training, integration and deployment of AI models. By creating an open source community around Machine Learning, AT&T will accelerate the transition to AI-based software across a wide range of industrial and commercial problems and reach a critical mass of applications. Acumos drives a data-centric process for creating machine learning applications.

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i2k2 Networks

Deploying a team of solution-oriented professionals with proven expertise in varied disciplines of technology, we offer tailored solutions for varied applications in exceedingly diverse environments. A dedicated web server from i2k2 provides you with the speed and security your websites demand - Linux Servers or Windows Servers. Dedicated server hosting with 99.95% guaranteed network up-time.

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STORAGE MANAGEMENT

Data Center as a Service Is the Way of the Future

Article | July 11, 2022

Data Center as a Service (DCaaS) is a hosting service that gives clients access to actual data center infrastructure and amenities. Through a Wide-Area Network, DCaaS enables clients to remotely access the provider's storage, server, and networking capabilities (WAN). Businesses can tackle their on-site data center's logistical and financial issues by outsourcing to a service provider. Many enterprises rely on DCaaS to overcome the physical constraints of their on-site infrastructure or to offload the hosting and management of non-mission-critical applications. Businesses that require robust data management solutions but lack the necessary internal resources can adopt DCaaS. DCaaS is the perfect answer for companies that are struggling with a lack of IT help or a lack of funding for system maintenance. Added benefits data Center as a Service allows businesses to be independent of their physical infrastructure: A single-provider API Data centers without Staff Effortlessly handle the influx of data Data centers in regions with more stable climates Data Center as a Service helps democratize the data center itself, allowing companies that could never afford the huge investments that have gotten us this far to benefit from these developments. This is perhaps the most important, as Infrastructure-as-a-Service enables smaller companies to get started without a huge investment. Conclusion Data center as a service (DCaaS) enables clients to access a data center remotely and its features, whereas data center services might include complete management of an organization's on-premises infrastructure resources. IT can be outsourced using data center services to manage an organization's network, storage, computing, cloud, and maintenance. The infrastructure of many businesses is outsourced to increase operational effectiveness, size, and cost-effectiveness. It might be challenging to manage your existing infrastructure while keeping up with the pace of innovation, but it's critical to be on the cutting edge of technology. Organizations may stay future-ready by working with a vendor that can supply DCaaS and data center services.

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IT SYSTEMS MANAGEMENT

Enhancing Rack-Level Security to Enable Rapid Innovation

Article | July 14, 2022

IT and data center administrators are under pressure to foster quicker innovation. For workers and customers to have access to digital experiences, more devices must be deployed, and larger enterprise-to-edge networks must be managed. The security of distributed networks has suffered as a result of this rapid growth, though. Some colocation providers can install custom locks for your cabinet if necessary due to the varying compliance standards and security needs for distinct applications. However, physical security measures are still of utmost importance because theft and social engineering can affect hardware as well as data. Risk Companies Face Remote IT work continue on the long run Attacking users is the easiest way into networks IT may be deploying devices with weak controls When determining whether rack-level security is required, there are essentially two critical criteria to take into account. The first is the level of sensitivity of the data stored, and the second is the importance of the equipment in a particular rack to the facility's continuing functioning. Due to the nature of the data being handled and kept, some processes will always have a higher risk profile than others. Conclusion Data centers must rely on a physically secure perimeter that can be trusted. Clients, in particular, require unwavering assurance that security can be put in place to limit user access and guarantee that safety regulations are followed. Rack-level security locks that ensure physical access limitations are crucial to maintaining data center space security. Compared to their mechanical predecessors, electronic rack locks or "smart locks" offer a much more comprehensive range of feature-rich capabilities.

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IT SYSTEMS MANAGEMENT

Infrastructure Lifecycle Management Best Practices

Article | July 6, 2022

As your organization scales, inevitably, so too will its infrastructure needs. From physical spaces to personnel, devices to applications, physical security to cybersecurity – all these resources will continue to grow to meet the changing needs of your business operations. To manage your changing infrastructure throughout its entire lifecycle, your organization needs to implement a robust infrastructure lifecycle management program that’s designed to meet your particular business needs. In particular, IT asset lifecycle management (ITALM) is becoming increasingly important for organizations across industries. As threats to organizations’ cybersecurity become more sophisticated and successful cyberattacks become more common, your business needs (now, more than ever) to implement an infrastructure lifecycle management strategy that emphasizes the security of your IT infrastructure. In this article, we’ll explain why infrastructure management is important. Then we’ll outline steps your organization can take to design and implement a program and provide you with some of the most important infrastructure lifecycle management best practices for your business. What Is the Purpose of Infrastructure Lifecycle Management? No matter the size or industry of your organization, infrastructure lifecycle management is a critical process. The purpose of an infrastructure lifecycle management program is to protect your business and its infrastructure assets against risk. Today, protecting your organization and its customer data from malicious actors means taking a more active approach to cybersecurity. Simply put, recovering from a cyber attack is more difficult and expensive than protecting yourself from one. If 2020 and 2021 have taught us anything about cybersecurity, it’s that cybercrime is on the rise and it’s not slowing down anytime soon. As risks to cybersecurity continue to grow in number and in harm, infrastructure lifecycle management and IT asset management are becoming almost unavoidable. In addition to protecting your organization from potential cyberattacks, infrastructure lifecycle management makes for a more efficient enterprise, delivers a better end user experience for consumers, and identifies where your organization needs to expand its infrastructure. Some of the other benefits that come along with comprehensive infrastructure lifecycle management program include: More accurate planning; Centralized and cost-effective procurement; Streamlined provisioning of technology to users; More efficient maintenance; Secure and timely disposal. A robust infrastructure lifecycle management program helps your organization to keep track of all the assets running on (or attached to) your corporate networks. That allows you to catalog, identify and track these assets wherever they are, physically and digitally. While this might seem simple enough, infrastructure lifecycle management and particularly ITALM has become more complex as the diversity of IT assets has increased. Today organizations and their IT teams are responsible for managing hardware, software, cloud infrastructure, SaaS, and connected device or IoT assets. As the number of IT assets under management has soared for most organizations in the past decade, a comprehensive and holistic approach to infrastructure lifecycle management has never been more important. Generally speaking, there are four major stages of asset lifecycle management. Your organization’s infrastructure lifecycle management program should include specific policies and processes for each of the following steps: Planning. This is arguably the most important step for businesses and should be conducted prior to purchasing any assets. During this stage, you’ll need to identify what asset types are required and in what number; compile and verify the requirements for each asset; and evaluate those assets to make sure they meet your service needs. Acquisition and procurement. Use this stage to identify areas for purchase consolidation with the most cost-effective vendors, negotiate warranties and bulk purchases of SaaS and cloud infrastructure assets. This is where lack of insights into actual asset usage can potentially result in overpaying for assets that aren’t really necessary. For this reason, timely and accurate asset data is crucial for effective acquisition and procurement. Maintenance, upgrades and repair. All assets eventually require maintenance, upgrades and repairs. A holistic approach to infrastructure lifecycle management means tracking these needs and consolidating them into a single platform across all asset types. Disposal. An outdated or broken asset needs to be disposed of properly, especially if it contains sensitive information. For hardware, assets that are older than a few years are often obsolete, and assets that fall out of warranty are typically no longer worth maintaining. Disposal of cloud infrastructure assets is also critical because data stored in the cloud can stay there forever. Now that we’ve outlined the purpose and basic stages of infrastructure lifecycle management, it’s time to look at the steps your organization can take to implement it.

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APPLICATION INFRASTRUCTURE

The Drive with Direction: The Path of Enterprise IT Infrastructure

Article | June 6, 2022

Introduction It is hard to manage a modern firm without a convenient and adaptable IT infrastructure. When properly set up and networked, technology can improve back-office processes, increase efficiency, and simplify communication. IT infrastructure can be utilized to supply services or resources both within and outside of a company, as well as to its customers. IT infrastructure when adequately deployed aids organizations in achieving their objectives and increasing profits. IT infrastructure is made up of numerous components that must be integrated for your company's infrastructure to be coherent and functional. These components work in unison to guarantee that your systems and business as a whole run smoothly. Enterprise IT Infrastructure Trends Consumption-based pricing models are becoming more popular among enterprise purchasers, a trend that began with software and has now spread to hardware. This transition from capital to operational spending lowers risk, frees up capital, and improves flexibility. As a result, infrastructure as a service (IaaS) and platform as a service (PaaS) revenues increased by 53% from 2015 to 2016, making them the fastest-growing cloud and infrastructure services segments. The transition to as-a-service models is significant given that a unit of computing or storage in the cloud can be quite cheaper in terms of the total cost of ownership than a unit on-premises. While businesses have been migrating their workloads to the public cloud for years, there has been a new shift among large corporations. Many companies, including Capital One, GE, Netflix, Time Inc., and others, have downsized or removed their private data centers in favor of shifting their operations to the cloud. Cybersecurity remains a high priority for the C-suite and the board of directors. Attacks are increasing in number and complexity across all industries, with 80% of technology executives indicating that their companies are unable to construct a robust response. Due to lack of cybersecurity experts, many companies can’t get the skills they need on the inside, so they have to use managed security services. Future of Enterprise IT Infrastructure Companies can adopt the 'As-a-Service' model to lower entry barriers and begin testing future innovations on the cloud's basis. Domain specialists in areas like healthcare and manufacturing may harness AI's potential to solve some of their businesses' most pressing problems. Whether in a single cloud or across several clouds, businesses want an architecture that can expand to support the rapid evolution of their apps and industry for decades. For enterprise-class visibility and control across all clouds, the architecture must provide a common control plane that supports native cloud Application Programming Interfaces (APIs) as well as enhanced networking and security features. Conclusion The scale of disruption in the IT infrastructure sector is unparalleled, presenting enormous opportunities and hazards for industry stakeholders and their customers. Technology infrastructure executives must restructure their portfolios and rethink their go-to-market strategies to drive growth. They should also invest in the foundational competencies required for long-term success, such as digitization, analytics, and agile development. Data center companies that can solve the industry's challenges, as well as service providers that can scale quickly without limits and provide intelligent outcome-based models. This helps their clients achieve their business objectives through a portfolio of 'As-a-Service' models, will have a bright future.

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Spotlight

i2k2 Networks

Deploying a team of solution-oriented professionals with proven expertise in varied disciplines of technology, we offer tailored solutions for varied applications in exceedingly diverse environments. A dedicated web server from i2k2 provides you with the speed and security your websites demand - Linux Servers or Windows Servers. Dedicated server hosting with 99.95% guaranteed network up-time.

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Lenovo Unveils New Offerings Purpose-Built for Analytics and AI Workloads, Delivers Business Intelligence

Lenovo | June 19, 2020

Lenovo Data Center Group. Our new ThinkSystem servers are designed to enhance mission-critical applications like SAP HANA and accelerate next-generation workloads like AI. The servers, combined with the DM7100 and business intelligence solutions from SAP, are material to helping customers address their data challenges in a variety of ways . A new remote deployment service offering for the Lenovo ThinkSystem DM7100 offers up to 80% faster implementation vs. scheduling on-site deployments. Today, Lenovo Data Center Group (DCG) announced new flexible solutions to empower customers to simplify common data management challenges. DCG is announcing the launch of the ThinkSystem SR860 V2 and SR850 V2 servers, which now feature 3rd Gen Intel Xeon Scalable processors with enhanced support for SAP HANA based on Intel Optane persistent memory 200 series. In addition, Lenovo is announcing new remote deployment service offerings for the ThinkSystem DM7100 storage systems. With these new offerings, customers can more easily navigate complex data management needs to deliver actionable business intelligence through artificial intelligence (AI) and analytics, while getting maximum results when combined with business applications like SAP HANA®. Many industries are faced with the ever-increasing challenge of having to analyze greater volumes of data, maintain the velocity of the data being transacted and support the variety of the data being collected and stored. Read more: DRAKE SUPERMARKETS CHOOSES NUTANIX HYPERCONVERGED INFRASTRUCTURE TO MODERNIZE THEIR IT ENVIRONMENT Lenovo’s new ThinkSystem SR860 V2 and SR850 V2 mission critical servers feature 3rd Gen Intel Xeon Scalable processors and support for enhanced Intel® Deep Learning Boost enabling customers to handle their most data intensive workloads. ~ Lenovo’s Without proper storage and processing capabilities, organizations are missing critical insights about their customers and business, while others experience bottlenecks due to a variety of data types that need to be analyzed, categorized and more quickly utilized to drive business value. Finally, the insights that come from data have definitive time limits, so the faster that systems can handle data, the greater amount of value that can be extracted. To help customers accelerate high performance workloads and improve efficiency, Lenovo’s new ThinkSystem SR860 V2 and SR850 V2 servers feature the latest in high-end processing and memory capabilities, with twice the amount of NVMe storage capacity1. The servers, combined with the DM7100 and business intelligence solutions from SAP, are material to helping customers address their data challenges in a variety of ways. The new 3rd Gen Intel Xeon Scalable processor-based enable more rapid data ingest capabilities to help tackle the growing volume of data coming into the data center. When combined with Lenovo DB fiber channel switches, customers can now achieve end-to-end NVMe deployment, delivering higher throughput and up to a 50 percent reduction in latency. The constant change in information and ever evolving needs of customers means there must be faster and more efficient solutions to turn data into information that empowers businesses,” said Kamran Amini, Vice President and General Manager of Server, Storage and Software Defined Infrastructure, Lenovo Data Center Group. “Our new ThinkSystem servers are designed to enhance mission-critical applications like SAP HANA and accelerate next-generation workloads like AI, analytics and machine learning, enabling mission critical performance and reliability for all data centers and maximum business value for our customers. Lenovo is a US$50 billion Fortune Global 500 company, with 63,000 employees and operating in 180 markets around the world. Focused on a bold vision to deliver smarter technology for all, we are developing world-changing technologies that create a more inclusive, trustworthy and sustainable digital society. By designing, engineering and building the world’s most complete portfolio of smart devices and infrastructure, we are also leading an Intelligent Transformation – to create better experiences and opportunities for millions of customers around the world. Select configurations of the ThinkSystem SR860 V2, SR850 V2 and DM7100 solutions are available through Lenovo TruScale, the pay-for-what-you-use data center, offering customers a flexible and cost effective option for adoption. Read more: DELL, MICROSOFT EMERGE REVENUE LEADERS IN DATA CENTER INFRASTRUCTURE

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Bringing the Power of Automation to DevOps with Artificial Intelligence and ML

DevOps | June 04, 2020

Artificial intelligence and ML can help us take DevOps to the next level through identifying problems more quickly and further automating our processes. The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. This automation frees up valuable IT resources to focus on innovative solutions, Here are three areas where AI and machine learning are advancing DevOps. The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. DevOps breeds efficiency through automating software delivery and allowing companies to push software to market faster while releasing a more reliable product. What is next for DevOps? We need to look no further than artificial intelligence and machine learning.Most organizations quickly realize the promise of AI and machine learning, but often fail to understand how they can properly harness them to improve their systems. That isn’t the case with DevOps. DevOps has some natural deficiencies that are difficult to solve without the computing power of machine learning and artificial intelligence. They are key to advancing your digital transformation. Here are three areas where AI and machine learning are advancing DevOps. As our technology stack grows, the complexity of our systems become increasingly magnified. Consider a distributed application architecture where IoT devices are contacting microservices running on a Kubernetes cluster. There are numerous potential points of failure, and data points are continuously logging every transaction. Sifting through massive data stores to pinpoint the root cause of an issue can be extremely time intensive for the team. Humans weren’t built for this kind of work. This is where artificial intelligence and machine learning thrive. With machine learning, we can build models to analyze patterns hidden within these mountains of data. Read more: INFRASTRUCTURE AS CODE VS. PLATFORM AS CODE It can recognize abnormalities, identify the underlying cause and provide suggestions for potential optimization. Through this predictive analysis, machine learning can not only help us identify problems eroding our systems. ~ DevOps It can recognize abnormalities, identify the underlying cause and provide suggestions for potential optimization. Through this predictive analysis, machine learning can not only help us identify problems eroding our systems, but also trap issues before they become problems. By performing early prediction and notification, we can address concerns as they step their way through the development pipeline, so few ever reach production. AI and machine learning can analyze usage data and security threats to help us optimize our applications. It can inspect user behavior to identify what application modules and functions are doing the heaviest lifting so we can focus our efforts on improving the user experience in these areas. We can also compare current releases to previous ones to be alerted to subtle performance degradations. Vendors are actively creating impressive tools to integrate with DevOps processes, Some IT departments are hoisting this responsibility on themselves, creating custom AI solutions tailored specifically to their business needs. . By continuously evaluating user behavior, AI can help us keep user experience at the forefront of our release planning. In tracking security threats with AI, we can readily see where hackers are trying to breach our systems so we can fortify our defenses. If a denial-of-service attack is directed at the organization, we can have a decision engine kick in to minimize the impact on the business. Rogue hackers aren’t the only threat AI can help reign in. It can churn through data in real time to spot fraudulent activity tied to unusual data patterns. There are no moral victories discovering $100,000 has been lost when an employee has been sypho DevOps brings automation and consistency to our release process. Try as it might, there are still areas that require a person to manage the process. With AI, we can continue to automate tedious, mundane tasks that are rife for human error. This automation frees up valuable IT resources to focus on innovative solutions.ning it off over the past year. Not only can we let AI automate our DevOps process, we can also take it a step further to self-heal problems without human intervention. Not ready to let the computers manage themselves? AI can recommend solutions for writing more efficient and performant code. It can even prioritize the anticipated impact of a change so the development team has direction when sizing up what should be addressed next. Some may say, we are essentially talking about AIOps. To a degree, this is true. Yet, the argument can be made that clear boundaries don’t exist marking where DevOps ends and AIOps begins. The overlap between the two can be significant, and AIOps is quickly becoming an indispensable part of the toolkit for DevOps practitioners. This isn’t Star Trek. We aren’t pondering about the technology of tomorrow. We can implement artificial intelligence and machine learning into our DevOps environment today. Vendors are actively creating impressive tools to integrate with DevOps processes. Some IT departments are hoisting this responsibility on themselves, creating custom AI solutions tailored specifically to their business needs. Read more: COMPARING SIX LEADING CONVERGED INFRASTRUCTURE VENDORS' PRODUCT

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Ford spins its self-driving business into a $4bn separate company

IoT Tech News | July 26, 2018

Automotive giant Ford has decided its self-driving efforts are important enough to warrant a separate company with $4 billion of investment. Ford Autonomous Vehicles LLC will be based in Detroit, Michigan and will be tasked with developing the company’s self-driving technology. Jim Hackett, President and CEO of Ford, explained the decision: “Ford has made tremendous progress across the self-driving value chain – from technology development to business model innovation to user experience. Now is the right time to consolidate our autonomous driving platform into one team to best position the business for the opportunities ahead.”

Read More

Lenovo Unveils New Offerings Purpose-Built for Analytics and AI Workloads, Delivers Business Intelligence

Lenovo | June 19, 2020

Lenovo Data Center Group. Our new ThinkSystem servers are designed to enhance mission-critical applications like SAP HANA and accelerate next-generation workloads like AI. The servers, combined with the DM7100 and business intelligence solutions from SAP, are material to helping customers address their data challenges in a variety of ways . A new remote deployment service offering for the Lenovo ThinkSystem DM7100 offers up to 80% faster implementation vs. scheduling on-site deployments. Today, Lenovo Data Center Group (DCG) announced new flexible solutions to empower customers to simplify common data management challenges. DCG is announcing the launch of the ThinkSystem SR860 V2 and SR850 V2 servers, which now feature 3rd Gen Intel Xeon Scalable processors with enhanced support for SAP HANA based on Intel Optane persistent memory 200 series. In addition, Lenovo is announcing new remote deployment service offerings for the ThinkSystem DM7100 storage systems. With these new offerings, customers can more easily navigate complex data management needs to deliver actionable business intelligence through artificial intelligence (AI) and analytics, while getting maximum results when combined with business applications like SAP HANA®. Many industries are faced with the ever-increasing challenge of having to analyze greater volumes of data, maintain the velocity of the data being transacted and support the variety of the data being collected and stored. Read more: DRAKE SUPERMARKETS CHOOSES NUTANIX HYPERCONVERGED INFRASTRUCTURE TO MODERNIZE THEIR IT ENVIRONMENT Lenovo’s new ThinkSystem SR860 V2 and SR850 V2 mission critical servers feature 3rd Gen Intel Xeon Scalable processors and support for enhanced Intel® Deep Learning Boost enabling customers to handle their most data intensive workloads. ~ Lenovo’s Without proper storage and processing capabilities, organizations are missing critical insights about their customers and business, while others experience bottlenecks due to a variety of data types that need to be analyzed, categorized and more quickly utilized to drive business value. Finally, the insights that come from data have definitive time limits, so the faster that systems can handle data, the greater amount of value that can be extracted. To help customers accelerate high performance workloads and improve efficiency, Lenovo’s new ThinkSystem SR860 V2 and SR850 V2 servers feature the latest in high-end processing and memory capabilities, with twice the amount of NVMe storage capacity1. The servers, combined with the DM7100 and business intelligence solutions from SAP, are material to helping customers address their data challenges in a variety of ways. The new 3rd Gen Intel Xeon Scalable processor-based enable more rapid data ingest capabilities to help tackle the growing volume of data coming into the data center. When combined with Lenovo DB fiber channel switches, customers can now achieve end-to-end NVMe deployment, delivering higher throughput and up to a 50 percent reduction in latency. The constant change in information and ever evolving needs of customers means there must be faster and more efficient solutions to turn data into information that empowers businesses,” said Kamran Amini, Vice President and General Manager of Server, Storage and Software Defined Infrastructure, Lenovo Data Center Group. “Our new ThinkSystem servers are designed to enhance mission-critical applications like SAP HANA and accelerate next-generation workloads like AI, analytics and machine learning, enabling mission critical performance and reliability for all data centers and maximum business value for our customers. Lenovo is a US$50 billion Fortune Global 500 company, with 63,000 employees and operating in 180 markets around the world. Focused on a bold vision to deliver smarter technology for all, we are developing world-changing technologies that create a more inclusive, trustworthy and sustainable digital society. By designing, engineering and building the world’s most complete portfolio of smart devices and infrastructure, we are also leading an Intelligent Transformation – to create better experiences and opportunities for millions of customers around the world. Select configurations of the ThinkSystem SR860 V2, SR850 V2 and DM7100 solutions are available through Lenovo TruScale, the pay-for-what-you-use data center, offering customers a flexible and cost effective option for adoption. Read more: DELL, MICROSOFT EMERGE REVENUE LEADERS IN DATA CENTER INFRASTRUCTURE

Read More

Bringing the Power of Automation to DevOps with Artificial Intelligence and ML

DevOps | June 04, 2020

Artificial intelligence and ML can help us take DevOps to the next level through identifying problems more quickly and further automating our processes. The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. This automation frees up valuable IT resources to focus on innovative solutions, Here are three areas where AI and machine learning are advancing DevOps. The automation wave has overtaken IT departments everywhere making DevOps a critical piece of infrastructure technology. DevOps breeds efficiency through automating software delivery and allowing companies to push software to market faster while releasing a more reliable product. What is next for DevOps? We need to look no further than artificial intelligence and machine learning.Most organizations quickly realize the promise of AI and machine learning, but often fail to understand how they can properly harness them to improve their systems. That isn’t the case with DevOps. DevOps has some natural deficiencies that are difficult to solve without the computing power of machine learning and artificial intelligence. They are key to advancing your digital transformation. Here are three areas where AI and machine learning are advancing DevOps. As our technology stack grows, the complexity of our systems become increasingly magnified. Consider a distributed application architecture where IoT devices are contacting microservices running on a Kubernetes cluster. There are numerous potential points of failure, and data points are continuously logging every transaction. Sifting through massive data stores to pinpoint the root cause of an issue can be extremely time intensive for the team. Humans weren’t built for this kind of work. This is where artificial intelligence and machine learning thrive. With machine learning, we can build models to analyze patterns hidden within these mountains of data. Read more: INFRASTRUCTURE AS CODE VS. PLATFORM AS CODE It can recognize abnormalities, identify the underlying cause and provide suggestions for potential optimization. Through this predictive analysis, machine learning can not only help us identify problems eroding our systems. ~ DevOps It can recognize abnormalities, identify the underlying cause and provide suggestions for potential optimization. Through this predictive analysis, machine learning can not only help us identify problems eroding our systems, but also trap issues before they become problems. By performing early prediction and notification, we can address concerns as they step their way through the development pipeline, so few ever reach production. AI and machine learning can analyze usage data and security threats to help us optimize our applications. It can inspect user behavior to identify what application modules and functions are doing the heaviest lifting so we can focus our efforts on improving the user experience in these areas. We can also compare current releases to previous ones to be alerted to subtle performance degradations. Vendors are actively creating impressive tools to integrate with DevOps processes, Some IT departments are hoisting this responsibility on themselves, creating custom AI solutions tailored specifically to their business needs. . By continuously evaluating user behavior, AI can help us keep user experience at the forefront of our release planning. In tracking security threats with AI, we can readily see where hackers are trying to breach our systems so we can fortify our defenses. If a denial-of-service attack is directed at the organization, we can have a decision engine kick in to minimize the impact on the business. Rogue hackers aren’t the only threat AI can help reign in. It can churn through data in real time to spot fraudulent activity tied to unusual data patterns. There are no moral victories discovering $100,000 has been lost when an employee has been sypho DevOps brings automation and consistency to our release process. Try as it might, there are still areas that require a person to manage the process. With AI, we can continue to automate tedious, mundane tasks that are rife for human error. This automation frees up valuable IT resources to focus on innovative solutions.ning it off over the past year. Not only can we let AI automate our DevOps process, we can also take it a step further to self-heal problems without human intervention. Not ready to let the computers manage themselves? AI can recommend solutions for writing more efficient and performant code. It can even prioritize the anticipated impact of a change so the development team has direction when sizing up what should be addressed next. Some may say, we are essentially talking about AIOps. To a degree, this is true. Yet, the argument can be made that clear boundaries don’t exist marking where DevOps ends and AIOps begins. The overlap between the two can be significant, and AIOps is quickly becoming an indispensable part of the toolkit for DevOps practitioners. This isn’t Star Trek. We aren’t pondering about the technology of tomorrow. We can implement artificial intelligence and machine learning into our DevOps environment today. Vendors are actively creating impressive tools to integrate with DevOps processes. Some IT departments are hoisting this responsibility on themselves, creating custom AI solutions tailored specifically to their business needs. Read more: COMPARING SIX LEADING CONVERGED INFRASTRUCTURE VENDORS' PRODUCT

Read More

Ford spins its self-driving business into a $4bn separate company

IoT Tech News | July 26, 2018

Automotive giant Ford has decided its self-driving efforts are important enough to warrant a separate company with $4 billion of investment. Ford Autonomous Vehicles LLC will be based in Detroit, Michigan and will be tasked with developing the company’s self-driving technology. Jim Hackett, President and CEO of Ford, explained the decision: “Ford has made tremendous progress across the self-driving value chain – from technology development to business model innovation to user experience. Now is the right time to consolidate our autonomous driving platform into one team to best position the business for the opportunities ahead.”

Read More

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