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7 Ways AI Speeds Up Software Development in DevOps

I am sure we all know that the need for speed in the world of IT is rising every day. The software development process that used to take much longer in the early stages is now being executed in weeks by collaborating distributed teams using DevOps methodologies. However, checking and managing DevOps environments involves an extreme level of complexity. The importance of data in todays’ deployed and dynamic app environments has made it tough for DevOps teams to absorb and execute data efficiently for identifying and fixing client issues. This is exactly where Artificial Intelligence and Machine Learning comes into the picture to rescue DevOps. AI plays a crucial role in increasing the efficiency of DevOps, where it can improve functionality by enabling fast building and operation cycles and offering an impeccable client experience on these features. Also, by using AI, DevOps teams can now examine, code, launch, and check software more efficiently. Furthermore, Artificial Intelligence can boost automation, address and fix issues quickly, and boost cooperation between teams. Here are a few ways AI can take DevOps to the next level. 1. Added efficiency of Software Testing The main point where DevOps benefits from AI is that it enhances the software development process and streamlines testing. Functional testing, regression testing, and user acceptance testing create a vast amount of data. And AI-driven test automation tools help identify poor coding practices responsible for frequent errors by reading the pattern in the data acquired by delivering the output. So, this type of data can be utilized to improve productivity. 2. Real-time Alerts Having a well-built alert system allows DevOps teams to address defects immediately. Prompt alerts enable speedy responses. However, at times, multiple alerts with the same severity level make it difficult for tech teams to react. AI and ML help a DevOps team to prioritize responses depending on the past behavior, the source of the alerts, and the depth. And can also recommend a prospective solution and help resolve the issue quicker. 3. Better Security Today, DDoS (Distributed Denial of Service) is very popular and continuously targets organizations and small and big websites. AI and ML can be used to address and deal with these threats. An algorithm can be utilized for differentiating normal and abnormal conditions and take actions accordingly. Developers can now make use of AI to improve DevSecOps and boost security. It consists of a centrally logging architecture for addressing threats and anomalies. 4. Enhanced Traceability AI enables DevOps teams to interact more efficiently with each other, particularly across long distances. AI-driven insights can help understand how specifications and shared criteria represent unique client requirements, localization, and performance benchmarks. 5. Failure Prediction Failure in a particular tool or any in area of DevOps can slow down the process and reduce the speed of the cycles. AI can read through the patterns and anticipate the symptoms of a failure, especially when a pre-happened issue creates definite readings. At the same time, the ML models can help predict an error depending on the data. AI can also see signs that we humans can’t notice. Therefore, these early notifications help the teams address and resolve the issues before impacting the SDLC (Software Development Life Cycle). 6. Even Faster Root Cause Analysis To find the actual cause of a failure, AI makes use of the patterns between the cause and activity to discover the root cause behind the particular failure. Engineers are often too preoccupied with the urgency to going Live and don’t investigate the failures thoroughly. Though they study and resolve issues superficially, they mostly avoid detailed root cause analysis. In such cases, the root cause of the issue remains unknown. Therefore, it is essential to conduct the root cause analysis to fix a problem permanently. And AI plays a crucial role here in these types of cases. 7. Efficient Requirements Management DevOps teams make use of AI and ML tools to streamline each phase of requirements management. Phases such as creating, editing, testing, and managing requirements documents can be streamlined with the help of AI. The AI-based tools identify the issues covering unfinished requirements to escape clauses, enhancing the quality and the accuracy of requirements. Wrapping Up Today, AI speeds up all phases of DevOps software development cycles by anticipating what developers need before even requesting for it. AI improves software quality by giving value to specific areas in DevOps, such as improved software quality with automated testing, automatically recommending code sections, and organizing requirement handling. However, AI must be implemented in a controlled manner to make sure that they become the backbone of the DevOps system and does not act as rogue elements that require continuous remediation.

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Beginners Guide to a Career in DevOps

ABSTRACTThe software development lifecycles moved from waterfall to agile models. These improvements are moving toward IT operations with evolution of Devops.DevOps primarily focuses on collaboration, communication, integration between developers and operations.AGILE EVOLUTION TO DEVOPSWaterfall model was based on a sequence starting with requirements stage, while development stage was under progress. This approach is inflexible and monolithic. In the agile process, both verification and validation execute at the same time. As developers become productive, business become more agile and respond to their customer requests more quickly and efficient.WHAT IS DEVOPSIt is a software development strategy which bridges the gap between the developers and IT Staff. It includes continuous development, continuous testing, continuous integration, continuous deployment, continuous monitoring throughout the development lifecycle.WHY DEVOPS IS IMPORTANT1.Short development cycle, faster innovation2.Reduced deployment failures, rollback and time to recover3.Improved communication4.Increased efficiencies5.Reduced costsWHAT ARE THE TECHNOLOGIES BEHIND DEVOPS?Collabration, Code Planning, Code Repository, Configuration Management, Continuous integration, Test Automation, Issue Tracking, Security, MonitoringHOW DOES DEVOPS WORKSDevOps uses a CAMS approachC=Culture, A=Automation, M=Measurement, S=SharingDEVOPS TOOLSTOP DEVOPS TESTING TOOLS IN 20191.Tricentis 2. Zephyr 3.Ranorex 4.Jenkins 5.Bamboo 6.Jmeter 7.Selenium 8.Appium 9.Soapui 10.CruiseControl 11.Vagrant 12.PagerDuty 13.Snort 14.Docker 15.Stackify Retrace 16.Puppet Enterprise 17.UpGuard 18.AppVerifyDEVOPS JOB ROLES AND RESPONSIBILITIESDevOps Evangelist – The principal officer (leader) responsible for implementingDevOps Release Manager – The one releasing new features & ensuring post-release product stabilityAutomation Expert – The guy responsible for achieving automation & orchestration of toolsSoftware Developer/ Tester – The one who develops the code and tests itQuality Assurance – The one who ensures the quality of the product confirms to its requirementSecurity Engineer – The one always monitoring the product’s security & healthDEVOPS CERITIFICATIONRet hat offers five courses with examDeveloping Containerized Applications, OpenShift Enterprise Administration, Cloud Automation with Ansible, Managing Docker Containers with RHEL Atomic Host, Configuration Management with PuppetAmazon web services offers the AWS certified DevOps EngineerSKILL THAT EVERY DEVOPS ENGINEER NEEDS FOR SUCCESS1.Soft Skills2.Broad understanding of tools and technologies2.1 Source Control (like Git, Bitbucket, Svn, VSTS etc)2.2 Continuous Integration (like Jenkins, Bamboo, VSTS )2.3 Infrastructure Automation (like Puppet, Chef, Ansible)2.4 Deployment Automation & Orchestration (like Jenkins, VSTS, Octopus Deploy)2.5 Container Concepts (LXD, Docker)2.6 Orchestration (Kubernetes, Mesos, Swarm)2.7 Cloud (like AWS, Azure, GoogleCloud, Openstack)3.Security Testing4.Experience with infrastructure automation tools5.Testing6.Customer-first mindset7.Collabration8.Flexibility9.Network awareness10.Big Picture thinking on technologiesLINKS:https://www.quora.com/How-are-DevOps-and-Agile-differenthttps://www.altencalsoftlabs.com/blog/2017/07/understanding-continuous-devops-lifecycle/https://jenkins.io/download/https://www.atlassian.com/software/bamboohttp://jmeter.apache.org/download_jmeter.cgihttp://www.seleniumhq.org/download/http://appium.io/https://www.soapui.org/downloads/download-soapui-pro-trial.htmlhttp://cruisecontrol.sourceforge.net/download.htmlhttps://www.vagrantup.com/downloads.htmlhttps://www.pagerduty.com/https://www.snort.org/downloadshttps://store.docker.com/editions/enterprise/docker-ee-trialhttps://saltstack.com/saltstack-downloads/https://puppet.com/download-puppet-enterprisehttps://www.upguard.com/demohttps://www.nrgglobal.com/regression-testing-appverify-download

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