Category: DevOps Toolbox

  • Atlassian Embeds Generative AI Across Cloud App Portfolio

    Atlassian Embeds Generative AI Across Cloud App Portfolio

    At its Team 23 conference today, Atlassian announced it is adding an artificial intelligence (AI) capability to the enterprise editions of its cloud portfolio to automate a wide range of routine tasks.

    Sherif Mansour, distinguished product manager for Atlassian, said Atlassian Intelligence uses natural language to drive a virtual agent that resolves routine help desk issues from within Slack or Microsoft Teams. Atlassian Intelligence can also identify incidents that have been previously resolved along with issues that are related to one another.

    In addition, IT teams can also leverage Atlassian Intelligence to create summaries for projects being tracked using the company’s Jira project management software, noted Mansour.

    The overall goal is to streamline incident management processes while also making it simpler for application stakeholders to collaboratively participate in software development, he added.

    Generative AI capabilities are likely to be infused into almost every imaginable application going forward, but Atlassian is clearly at the forefront of an effort to leverage AI to improve productivity. For example, instead of spending time handling routine issues that increase the amount of daily toil, IT teams should be able to spend more time resolving more complex issues, noted Mansour.

    The cumulative effect of AI should result in a significant decrease in the backlog of tasks that many IT teams would otherwise have to manually perform, he noted.

    It’s not clear just yet how much generative AI will impact the way software is developed and how IT is managed, but it’s already apparent that fundamental change is on the way. The Atlassian Intelligence approach is intended to augment the capabilities of IT teams so they can do more with the same number of or fewer personnel.

    The challenge IT teams will face is identifying tasks they couldn’t previously accomplish simply because there wasn’t enough time available.

    Fundamentally, generative AI changes the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. That makes it possible for anyone to make use of large language models used to create generative AI platforms to, for example, automate a routine task or, one day soon, create code.

    At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of IT organizations, the ultimate impact should involve less drudgery as many of the tasks that make managing IT tedious are eliminated.

    Hopefully, as IT continues to evolve, there will still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has been for the past four decades or more.

  • AI Chatbots: Game Changers for DevOps Transformations

    AI Chatbots: Game Changers for DevOps Transformations

    In today’s fast-paced digital world, organizations are constantly looking for ways to accelerate their software development processes and ensure the seamless integration of their development and operations teams. DevOps has emerged as a popular methodology to achieve this goal, but even with its adoption, many companies still face challenges in their transformation journey. AI chatbots can revolutionize DevOps transformations, making them more efficient and effective than ever before.

    How AI ChatBots Improve DevOps Transformations

    Enhance collaboration by automating routine tasks and facilitating communication between teams: AI chatbots can handle repetitive tasks, freeing up team members to focus on more critical issues while also providing a centralized communication hub for both development and operations teams.
    Expedite incident management by providing real-time alerts and smart recommendations: AI chatbots can analyze logs and system metrics to quickly identify issues and suggest the most appropriate resolution steps, reducing downtime and improving overall system performance.
    Streamline deployment and monitoring processes using AI-driven automation: AI chatbots can intelligently manage deployment pipelines, ensuring that the right code gets deployed to the right environments while also monitoring system health and performance in real-time.
    Offer real-time analytics and insights into the development pipeline for informed decision-making: AI chatbots can gather and analyze data from various sources, providing valuable insights that help teams make better decisions related to code quality, release schedules and resource allocation.
    Identify skill gaps and recommend targeted training resources for continuous improvement: AI chatbots can analyze team performance and individual skill sets, helping organizations identify areas for improvement and providing personalized learning resources to close those gaps.

    Roadmap to Implement AI Chatbots in DevOps Transformations

    Evaluate your organization’s current DevOps maturity and identify areas for improvement: By assessing your existing processes and infrastructure, you can better understand the gaps where AI chatbots can make the most significant impact.
    Select an AI chatbot platform that aligns with your organization’s technology stack and goals: Choose a platform that complements your existing tools and supports your specific DevOps objectives to ensure seamless integration and adoption.
    Define and prioritize use cases for AI chatbot implementation in your DevOps processes: Identify the key areas where AI chatbots can provide the most value, such as incident management or deployment automation and prioritize their implementation accordingly.
    Develop a phased implementation plan that includes integration, testing and deployment of AI chatbots: Create a step-by-step plan to integrate AI chatbots into your existing DevOps processes, ensuring proper testing and validation at each stage to minimize potential disruptions.
    Train your teams on how to leverage AI chatbots effectively and continually refine their usage based on feedback and performance metrics: Provide education and training resources to help your team members understand how to use AI chatbots to their full potential and foster a culture of continuous improvement.

    Benefits of AI Chatbots in DevOps Transformations

    AI chatbots offer several benefits that can significantly improve DevOps transformations, including:

    Increased efficiency by automating manual tasks and streamlining processes: This leads to faster development cycles, quicker releases and a more agile response to changing business requirements.
    Enhanced collaboration and communication among development and operations teams: AI chatbots can break down silos between teams, fostering a more collaborative and unified approach to software development and delivery.
    Faster incident resolution and reduced downtime: With AI chatbots’ help, issues can be identified and resolved more quickly, minimizing the impact on end users and maintaining a high level of service quality.
    Real-time insights into the development pipeline for more informed decision-making: AI chatbots enable teams to make data-driven decisions by providing real-time analytics and insights into various aspects of the development process, such as code quality, resource allocation and release timelines.
    Continuous learning and improvement through AI-driven recommendations: By identifying skill gaps and suggesting targeted training resources, AI chatbots can help teams continuously improve their knowledge and expertise, ultimately leading to better performance and more successful DevOps transformations.

    Summary

    In conclusion, AI chatbots are poised to revolutionize DevOps transformations by addressing common challenges, enhancing efficiency and fostering a culture of continuous improvement. By automating routine tasks, facilitating communication and providing valuable insights into the development pipeline, they can significantly improve the effectiveness of DevOps processes. As organizations continue to adopt and refine their DevOps strategies, incorporating AI chatbots into their workflows will become increasingly essential to remain competitive in the ever-evolving digital landscape.

  • Mobb Launches Community Edition of Automated Remediation Tool

    Mobb Launches Community Edition of Automated Remediation Tool

    Mobb today made available a free community edition of a namesake tool that creates fixes to open source vulnerabilities. The fixes are based on the results of code scanning by a static application security testing (SAST) tool.

    Fresh from raising $5.4 million in seed funding, Mobb CEO Eitan Worcel said the company developed a tool that creates validated patches based on the scan results of third-party SAST tools from Checkmarx, GitHub and Snyk. Initially, the tool is aimed at Java vulnerabilities.

    Worcel said that most vulnerabilities can be traced back to eight types of mistakes that developers routinely make. The company created a pattern for those common mistakes, along with patterns to fix them, called Mobb rules. That ability enables Mobb to accurately produce a code fix that both remediates the vulnerability and adheres to the correctness of the language to eliminate code defects. Mobb then makes available a patch to address those issues in a way that can be readily downloaded and applied by developers. Mobb does not automatically apply those fixes because most developers prefer to review them first, noted Worcel.

    The Mobb approach eliminates the need for IT teams to sort through SAST scans themselves, which can result in the discovery of thousands of vulnerabilities that might theoretically need to be fixed. Each vulnerability, at minimum, is going to require 30 minutes to fix, so the Mobb platform provides a better return on investment and reduces patch backlogs, added Worcel.

    For decades, application development teams have been attempting to find a way to remediate vulnerabilities as quickly as possible. With increased focus on adopting DevSecOps best practices to address those vulnerabilities, more organizations are now reviewing which types of vulnerabilities should have a higher remediation priority.

    Historically, one of the reasons for the wide divide between application development teams and cybersecurity professionals is that many of the vulnerabilities being discovered don’t actually impact applications running in production environments. Development teams then find themselves wasting time investigating vulnerabilities and, when they do determine a vulnerability is an issue, spending time developing a patch. The Mobb tool addresses that later issue by automatically creating the necessary patch, said Worcel.

    Many organizations just allocate junior developers to the task of remediating vulnerabilities, which Worcel noted frees up more experienced developers to focus on writing additional business logic.

    There will, of course, be vulnerabilities that the Mobb tool will not be able to address simply because there may not be enough affected applications to warrant pre-building a patch. Even with the most robust set of rules, it is improbable that an automated tool can, with 100% certainty, safely fix every issue. However, given the number of routine vulnerabilities present in applications, such as SQL injections, there’s plenty of opportunity to improve developer productivity by relying on patches that have already been developed.

    One way or another, the process by which patches are prioritized, developed and then applied has long been a source of DevOps frustration. Anything that streamlines that process will go a long way toward improving the overall state of application security at a time when cybercriminals are becoming more adept at exploiting vulnerabilities wherever they may be found.

  • Endor Labs Taps ChatGPT to Identify Secure Open Source Software

    Endor Labs Taps ChatGPT to Identify Secure Open Source Software

    Endor Labs has launched DroidGPT, an extension of its software for assessing risks in open source code. DroidGPT integrates the ChatGPT generative artificial intelligence (AI) platform to make it simpler to discover the most secure version of an open source package.

    That capability makes it possible for developers to launch a natural language query from within the Endor Labs platform that asks ChatGPT to identify, for example, the most secure logging modules of Java applications.

    Endor Labs CEO Varun Badhwar said the goal is to make it simpler to apply guardrails to application development processes that today rely heavily on reusing open source packages. Developers often wind up using an older version of those packages that are not as secure because a known vulnerability has not been remediated.

    Endor Labs’ Dependency Lifecycle Management platform applies graph analysis to identify the depth of dependencies that exist within an application. That capability makes it simpler to identify where vulnerable components have actually been employed within an application. DroidGPT extends that capability to identify the most secure versions of those components that developers should be using, said Badhwar.

    Having a full understanding of their dependency graph also lets customers generate and analyze accurate software bills of materials (SBOMs) as applications are dynamically updated, noted Badhwar.

    The level of dependency on open source software packages to create applications has risen sharply over the years. An analysis of nearly 2,000 software packages published by Endor Labs found 95% of all application vulnerabilities can be traced back to a transitive dependency created when a developer used an open source component. Functional dependencies are created whenever developers download a third-party component, so it is crucial to assess the risk levels created by those dependencies.

    Fortunately, in the wake of a series of high-profile breaches, there has been increased focus on securing software supply chains. The ongoing challenge is most developers don’t have a lot of cybersecurity expertise, so as contributions are made to open source projects it’s relatively easy for mistakes to be made. The simple truth is many applications deployed in production environments are riddled with known vulnerabilities that have yet to be addressed. DroidGPT is designed to make it simpler to begin the process of remediating software vulnerabilities in applications as they are built and after they are deployed.

    There is no doubt that it will take years before organizations are able to implement a set of truly mature DevSecOps best practices to teach developers how to build more secure applications. That journey, however, needs to begin with tools that enable them to address fundamental issues that dependencies inevitably create. After all, the best way to combat application vulnerabilities is to make sure they don’t manifest themselves in code in the first place. Making it easier to update applications using older versions of open source modules is nothing less than critical at a time when cybercriminals are getting more adept at exploiting them.

  • PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty this week made generally available an artificial intelligence for IT operations (AIOps) platform that leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates.

    Jonathan Rende, senior vice president and general manager for PagerDuty, said that approach enables the PagerDuty AIOps platform to begin surfacing valid recommendations sooner than other AIOps platforms. Other platforms first need to be integrated into IT environments by an expensive team of consultants and integrators, he added. The PagerDuty approach makes the benefits of AIOps immediately available to both IT operations teams and application development teams alike, he said.

    The arrival of PagerDuty AIOps comes on the heels of the launch of PagerDuty Process Automation, a platform for orchestrating automation across multiple platforms that is integrated within PagerDuty Operations Cloud. That capability makes it possible for organizations to centralize the management of islands of automation that have emerged across the enterprise without having to replace any framework that has already been deployed, said Rende. There are also more than 700 integrations between PagerDuty AIOps and the PagerDuty Operations Cloud platform.

    Like other platforms, PagerDuty AIOps relies on machine learning algorithms and an AI model created by PagerDuty to correlate events so that the platform can put them in context. Incident response recommendations are then made, which an IT team can accept and automatically implement or reject. The platform then uses those inputs to surface more accurate recommendations over time, said Rende.

    The platform will also automatically group apply defined rules coupled with its machine learning capabilities to aggregate alerts. That capability reduces the overall alert noise that might otherwise be generated, noted Rende.

    Organizations are looking to invest in AI and automation more heavily at a time when many of them are reducing the overall size of their IT staff, said Rende. The challenge with AI, however, is that it can take six months or more for a rival AIOps platform to learn enough about an IT environment to begin making recommendations. The PagerDuty approach takes advantage of the data within the PagerDuty incident management platform to train an AI model more quickly, in a way that reduces the time to generate valid recommendations to a matter of days, noted Rende.

    PagerDuty has been building that AI model for the last four and a half years with that specific goal in mind, he added.

    It’s not clear how quickly organizations are embracing AIOps, but as IT environments continue to evolve, many once-manual tasks are being automated. There is a natural fear of the impact those advances might have on specific roles within an IT organization, but as IT environments get more complex, there will always be a need for IT personnel that can focus on higher-order tasks, said Rende.

    Overall, the rate of IT staff turnover should also decline as much of the toil that contributes to burnout is also eliminated.

    It may be a while yet before AI becomes pervasively applied to manage IT, but the proverbial genie is not going back into the bottle. The challenge is determining precisely where the line between the man-machine interface with IT environments is today versus where it might be tomorrow.

  • GitKraken App Correlates Activity Across Jira Cloud and Git Repositories

    GitKraken App Correlates Activity Across Jira Cloud and Git Repositories

    GitKraken today made available a preview of a free application that aggregates activity across Atlassian’s Jira Cloud and any Git repository to provide a unified view across all the activities of a software development team.

    Adam Wride, general manager for planning solutions of GitKraken, said Team Insights for Jira (TIJ) will make it easier for DevOps teams, project managers and application stakeholders to collaborate.

    TIJ accomplishes that goal by providing an overlay using a familiar Gannt chart view of epics and issues found in project management applications to correlate, for example, user activity such as status changes and comments made in Jira Cloud with updates to commits and pull requests made in a Git repository, he noted.

    Team members can then filter those views by project, sprint, contributors, issue type and other criteria to drill down on the exact list of issues that need to be addressed, added Wride.

    Most application development efforts rarely follow the original DevOps workflow plan, so TIJ provides a means for organizations to track real-time activity data to visualize how their efforts are progressing against the plan. That makes it possible to make data-driven decisions about how to reallocate resources as needed, said Wride.

    At the core of TIJ is a Git Integration tool that GitKraken previously made available. TIJ is the second offering from the company based on that tool following the launch of an application that integrates Jira with continuous integration/continuous delivery (CI/CD) platforms. GitKraken claimed there are already more than 10,000 organizations employing its Git integration tool.

    As more organizations become dependent on software to drive a wide range of processes, the need for increased visibility into DevOps workflows has become more crucial. Revenue is now directly tied to the pace at which software is being deployed and updated. Any change to the pace at which an application is being developed needs to be communicated across all the stakeholders that might be impacted. The challenge is that given all the dependencies that exist within modern applications, it’s not always easy to discern how a delay in the development of one module or component might impact multiple application development projects.

    Of course, project managers have been using project management applications to bring some order to the chaos of software development for decades. The challenge has always been that manual updates to projects have not always been provided in a timely manner. Developers don’t always remember to update project managers, who must manually enter updates in a project management application. GitKraken is making a case for an approach that automatically updates Jira Cloud by collecting data from DevOps tools and platforms rather than waiting for software engineers to remember to share that information with a project manager.

    Arguably, there’s now no excuse for everyone in an organization—if they care to, they can find out the status of any application development initiative. The challenge is making sure the data that DevOps teams—and their tools and platforms—already generate seamlessly updates the project management software that everyone else relies on to keep track of those projects.

  • Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Instead of AI aids for programming, what about for debugging? The pseudonymous “BioBootloader” says he’s persuaded GPT-4 to make his code self heal. (more…)

  • Fiberplane Adds Open Source Libraries to Automatically Collect Metrics

    Fiberplane Adds Open Source Libraries to Automatically Collect Metrics

    Fiberplane today added an ability to automatically collect metrics in real-time and make them accessible either via its namesake notebook software for debugging infrastructure or an open source Grafana dashboard.

    Fiberplane CEO Micha Hernandez van Leuffen said Autometrics is an open source set of libraries the company created to make it simpler for developers to see platform performance metrics using a set of tools embedded within their integrated development environment (IDE).

    The goal is to reduce the cognitive load on developers; today, developers need to first determine what metrics to track, find a way to collect the appropriate data and then launch queries against those metrics to determine the root cause of an issue, said Hernandez van Leuffen.

    Autometrics, in contrast, provides access to a set of common queries using the Prometheus programming language (PromQL) against data that has been collected using open source OpenTelemetry agent software, he noted.

    That approach, for example, makes it simpler to track error rates, response time and latency of any function. Links to live charts based on real-time data can also be inserted into the comments section of any document a development team creates.

    The Fiberplane notebook uses the same operational transformation algorithm embedded within Google Docs to enable teams to easily collaborate around a document or spreadsheet. The core platform itself is built in Rust and WebAssembly (Wasm) to make it simpler to integrate Fiberplane as a plug-in into an existing DevOps workflow.

    Autometrics extends that capability in a way that enables DevOps teams to provide developers with instant access to observability data. They can then see how their code is performing on any given platform, which is critical as more responsibility for running applications continues to shift left, said Hernandez van Leuffen. Today, many developers need to wait for an IT operations team to share data collected via an observability platform that was designed to be used by ITOps rather than a developer, he added.

    As observability capabilities become more federated across the entire software development life cycle, it’s critical to make sure developers and IT operations teams can see metrics being generated in real-time. Otherwise, confusion arises because teams are not looking at the same set of metrics.

    Fiberplane address that issue by taking the concept of notebooks—often used by data science teams to collaborate—and applying it to DevOps workflows to reduce the amount of time it takes to resolve any issue. As IT environments become more distributed in the age of the cloud and microservices, troubleshooting issues invariably spans the responsibilities of multiple members of an IT team. In effect, a notebook makes it possible to spin up a virtual war room on demand that enables the members of an IT organization to collaborate—regardless of where they are physically located or which organization they may be employed by.

  • Harnessing the Power of AI Chatbots for Continuous Testing

    Harnessing the Power of AI Chatbots for Continuous Testing

    Artificial intelligence (AI) is revolutionizing the way we develop and test software, especially with the advent of AI chatbots. These intelligent agents have the potential to streamline and automate continuous testing while improving software quality. However, software and QA managers must understand the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes. This blog explains crucial aspects to help your team harness the full potential of AI-driven continuous testing.

    The Urgency of Integrating AI Chatbots Into Continuous Testing

    • Increasing complexity of software applications: Modern software applications are becoming increasingly complex, which makes manual testing time-consuming, error-prone and costly. AI chatbots can analyze vast amounts of data in real-time, identify patterns and learn from the testing process, making them invaluable for continuous testing.
    • Fast-paced development and market demands: Today’s competitive market demands faster release cycles and shorter time-to-market. AI chatbots can help you accelerate testing and development cycles, allowing your team to focus on delivering high-quality software promptly.
    • The growing importance of customer experience: AI chatbots can simulate user interactions and predict user behavior, helping to improve user experiences by identifying potential issues before they become real problems.

    Opportunities for AI Chatbots in Continuous Testing

    • Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy.
    • Enhanced defect detection: AI chatbots can identify defects early in the development process, reducing the overall cost and time spent fixing them.
    • Efficient resource allocation: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively, leading to increased productivity.

    Pitfalls to Avoid When Implementing AI Chatbots

    • Over-reliance on AI: AI chatbots are powerful tools, but they should not replace human testers completely. Human intuition, creativity and critical thinking are essential for effective testing.
    • Insufficient training data: To work effectively, AI chatbots require quality training data. Ensure that your chatbot is trained with diverse and representative datasets to minimize the risk of biased or inaccurate results.
    • Integration challenges: Integrating AI chatbots with existing systems and processes can be challenging. Ensure that your team has a clear understanding of the integration requirements and potential issues that may arise.

    Best Practices for Applying AI Chatbots to Continuous Testing

    • Define clear objectives: Establish clear goals and expectations for your AI chatbot to ensure that it aligns with your team’s overall testing strategy.
    • Choose the right AI chatbot platform: Consider factors like cost, scalability, ease of integration and support for programming languages when selecting an AI chatbot platform.
    • Continuously monitor and refine: Continuously monitor your AI chatbot’s performance and make necessary adjustments to improve its effectiveness and efficiency.
    • Foster collaboration: Encourage collaboration between human testers and AI chatbots to leverage the strengths of both.

    Benefits of Applying AI Chatbots to Continuous Testing

    • Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy. By automating repetitive and complex tasks, AI chatbots can effectively handle edge cases and efficiently test various scenarios, thereby reducing the likelihood of undetected issues in the software.
    • Enhanced defect detection and early issue identification: AI chatbots can quickly identify defects and potential issues early in the development process, reducing the overall cost and time spent on fixing them. By leveraging machine learning and pattern recognition, AI chatbots can predict potential problems and bring them to the attention of developers, allowing for proactive resolution and improved software quality.
    • Efficient resource allocation and increased productivity: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively. Human testers can focus on higher-level tasks that require creativity, critical thinking, and domain expertise. This leads to increased productivity, as the team can concentrate on delivering high-quality software and improving user experiences.

    Conclusion

    AI chatbots are transforming the landscape of continuous testing, offering unprecedented opportunities to improve software quality and speed up development cycles. By understanding the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes, software managers can effectively harness the potential of these intelligent agents and drive their teams toward success.

  • 5 Best Practices for Using VMs on Azure Cloud

    5 Best Practices for Using VMs on Azure Cloud

    When beginning a cloud improvement/optimization project, an easy place to start is to focus on the initial low-hanging fruit of your virtual machine (VM) environments. This article takes a closer look at these steps, using a Microsoft Azure VM environment as an example.

    Here are five best practices that can be easily achieved when using VMs on Azure cloud.

    1. Properly Size Your Virtual Machines: To maximize performance and minimize costs, it’s important to size your VMs appropriately. You can use the Azure portal to determine the right size for your workloads and then select the right VM size based on the CPU, memory and storage requirements of your application. For example, you can use the Azure VM sizing calculator tool to get an estimate of the virtual machine size based on your workload requirements.

    Let’s say you want to host a web application that requires 2 vCPUs, 8 GB of memory and 50 GB of storage. You can use the Azure VM sizing calculator tool to determine the appropriate VM size based on these requirements. The tool may suggest using a “Standard B2s” VM which has 2 vCPUs, 4 GB of memory and a maximum disk size of 32 GB.

    In this case, you would need to increase the disk size to 50 GB. You can do this by adding a data disk to the VM or using a larger VM size with enough disk space. For example, you could use a “Standard B4ms” VM with 4 vCPUs, 16 GB of memory, and a maximum disk size of 64 GB.

    It’s important to note that the actual VM size may vary based on your workload requirements and the specifics of your application. The Azure VM sizing calculator tool provides a helpful starting point, but you should also perform your own testing and validation to ensure that the VM size meets your needs.

    2. Use Managed Disks: Managed disks provide increased reliability, scalability and security compared to traditional storage accounts. For example, you can use managed disks to automatically scale the disk size and performance as the data grows and to replicate data for disaster recovery.

    For example, to create a 100 GB managed disk, follow these steps:

    • In the Azure portal, go to the “Disks” section and click on “Add.”
    • Select “Blank disk” as the source and specify the disk size as 100 GB.
    • Choose the appropriate storage account type (Premium or Standard), replication options and disk type (HDD or SSD).
    • Give the disk a name and create it in the same resource group as your VM.
    • Once the disk is created, you can attach it to your VM by going to the virtual machine’s “Disks” section, clicking on “Attach,” and selecting the newly created disk. By using managed disks, you benefit from automatic disk management and replication as well as improved reliability and security compared to traditional storage accounts. Additionally, managed disks simplify the process of attaching and detaching disks to and from VMs, making it easier to manage your storage resources.

    3. Enable Monitoring and Diagnostics: To monitor the health and performance of your VMs, you can use Azure Monitor which provides real-time insights into resource utilization, application performance and other key metrics. You can also configure alerts to notify you of potential issues and diagnose problems using Azure Log Analytics. For example, you can set up an alert to notify you if the CPU usage of a VM exceeds a certain threshold.

    You can also use Azure Monitor to gain real-time insights into the virtual machine’s resource utilization, application performance and other key metrics by following these steps:

    • In the Azure portal, go to the virtual machine’s “Monitoring” section.
    • Click on “Diagnostic settings” and then click on “Add diagnostic setting.”
    • Give the diagnostic setting a name, select the VM you want to monitor and choose the metrics and logs you want to collect.
    • Choose the storage account you want to use to store the monitoring data and specify the retention policy for the data.
    • Click “Save” to apply the changes.

    By enabling monitoring and diagnostics for your virtual machines, you can proactively monitor the health and performance of your VMs and quickly identify and resolve potential issues. Additionally, you can use the collected data to generate reports, visualize trends and perform analysis to gain deeper insights into the performance of your VMs.

    4. Implement Security Best Practices: To secure your virtual machines, you can use Azure Security Center to implement security best practices and monitor for potential threats. You can also use Azure Active Directory to control access to VMs and secure your identity management. For example, you can use Azure Security Center to assess the security of your VMs and implement security recommendations such as enabling network security group (NSG) rules to restrict inbound and outbound traffic.

    If you have a VM that you want to connect to other resources in your Azure environment, you can use a managed virtual network to provide a secure and scalable networking solution for your VM.

    Here is how to create a managed virtual network in Azure:

    • In the Azure portal, go to the “Virtual network” section and click on “Add.”
    • Give the virtual network a name, select the appropriate subscription and resource group and specify the address space.
    • Create one or more subnets within the virtual network, and specify the subnet address range and subnet name.
    • Associate the virtual network with a network security group (NSG) to control inbound and outbound traffic.
    • Create a network interface for your VM and associate it with the virtual network and subnet you created.

    By using a managed virtual network, you can create a secure and isolated network environment for your VM and connect it to other resources in your Azure environment. Additionally, you can use the virtual network to control and manage network traffic and implement advanced networking scenarios such as load balancing, VPN connectivity and more.

    5. Keep Virtual Machines Up-to-Date: Regularly updating your VMs with the latest security patches and software updates helps ensure that they are protected against potential threats and vulnerabilities. You can use Azure Update Management to automate the update process and keep your VMs updated. For example, you can schedule regular updates for your VMs to ensure they always have the latest security patches and software updates installed.

    When planning public cloud environments, two commonly used words are “progress” and “results.” Following the best practices above will help position your cloud journey for success.