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	<title>AIOps Archives - CDInsights</title>
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		<title>Explore the Mutual Advantages of Generative AI and the Cloud</title>
		<link>https://www.clouddatainsights.com/explore-the-mutual-advantages-of-generative-ai-and-the-cloud/</link>
					<comments>https://www.clouddatainsights.com/explore-the-mutual-advantages-of-generative-ai-and-the-cloud/#respond</comments>
		
		<dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator>
		<pubDate>Thu, 08 Jun 2023 20:47:16 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[cloud operations]]></category>
		<category><![CDATA[generative AI]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=3322</guid>

					<description><![CDATA[The convergence of generative AI and the cloud offers mutual benefits for both, as well as new opportunities for businesses.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><img fetchpriority="high" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_180698848_S.jpg" alt="The convergence of generative AI and the cloud offers mutual benefits for both, as well as new opportunities for businesses." class="wp-image-3323" width="750" height="500" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_180698848_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_180698848_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_180698848_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_180698848_S-930x620.jpg 930w" sizes="(max-width: 750px) 100vw, 750px" /><figcaption class="wp-element-caption"><em>The cloud and generative AI make each other better.</em></figcaption></figure></div>


<p>Google Cloud has announced&nbsp;<a href="https://www.cnbc.com/2023/06/07/google-cloud-partners-with-mayo-clinic-brings-generative-ai-to-health.html" target="_blank" rel="noreferrer noopener">a partnership</a>&nbsp;with the Mayo Clinic to bring a generative AI into&nbsp;healthcare use cases. Healthcare organizations will be able to build customized chatbots and speed up diagnosis. And they aren&#8217;t the only ones. Both&nbsp;<a href="https://www.ciodive.com/news/AWS-Microsoft-Google-cloud-infrastructure-AI-ML-compute/649712" target="_blank" rel="noreferrer noopener">AWS and Microsoft</a>&nbsp;have added generative AI capabilities to their offerings. And according to a report from&nbsp;<a href="https://www.globenewswire.com/en/news-release/2023/04/28/2657247/28124/en/2023-Growth-Opportunities-for-Artificial-Intelligence-in-the-Public-Cloud-Generative-AI-Drives-the-Opportunity-Universe-with-New-Intelligent-Capabilities.html" target="_blank" rel="noreferrer noopener">ResearchandMarkets</a>, the convergence of generative AI and the cloud is fueling unique opportunities for businesses in a variety of use cases.&nbsp;&nbsp;</p>



<p>See also: <a href="https://www.clouddatainsights.com/nailing-ai-from-cloud-to-the-edge/" target="_blank" rel="noreferrer noopener">Nailing AI from Cloud to the Edge</a></p>



<p>This interest in a generative AI/cloud convergence in recent years has a reason. Both Generative Artificial Intelligence (AI) and cloud computing have revolutionized the IT landscape, individually reshaping industries and delivering unprecedented capabilities for new technology tools. Let&#8217;s explore the profound impacts that generative AI has on the cloud and, conversely, how the cloud empowers and enhances generative AI capabilities.</p>



<h3 class="wp-block-heading">The cloud unlocks the full power of generative AI for business use cases</h3>



<p>The cloud offers several significant enhancements for generative AI, specifically in business use cases:</p>



<ol class="wp-block-list">
<li><strong>Scalability:</strong> Generative AI models often require substantial computational resources, especially during the training phase. Cloud platforms allow companies to scale up or down dynamically, allowing IT teams to allocate resources as needed. This scalability ensures that organizations can handle the computational demands of training large-scale generative AI models without needing to invest in expensive on-premises infrastructure if they don&#8217;t want to.</li>



<li><strong>Cost-effectiveness:</strong> Cloud computing operates on a pay-as-you-go model, which offers companies what they want most, choices. Instead of the traditional processing stack, which is rigid and can waste resources at times and others, constrict processing, companies can implement a more flexible approach. With the cloud, businesses can provision resources on demand, avoiding the need for expensive hardware investments and reducing <a href="https://www.clouddatainsights.com/youve-migrated-to-the-cloud-now-what-4-critical-cost-saving-practices/" target="_blank" rel="noreferrer noopener">operational costs</a>.</li>



<li><strong>Accessibility:</strong> The cloud democratizes access to generative AI capabilities, making them more accessible to businesses of all sizes. Instead of developing and maintaining their own infrastructure, companies can leverage cloud-based AI services and platforms. This access levels the playing field for smaller companies without extensive AI teams or deep pockets for IT investments. It can also allow companies of all sizes to start with small generative AI projects to see if they&#8217;re suitable for a particular project or business need.</li>



<li><strong>Collaboration and Knowledge Sharing:</strong> Creating and deploying generative AI projects often involves collaboration among data scientists, researchers, and engineers. Cloud platforms offer excellent collaboration tools, version control systems, and shared development environments, allowing teams to work together seamlessly instead of arguing over which version is the most recent and missing important information because of silos. Cloud-based services also enable easy code sharing, debugging, and project management, which dramatically accelerates the development and deployment of generative AI models.</li>



<li><strong>Data Management:</strong> Generative AI models require large volumes of training data. Cloud-based data storage and management solutions provide businesses with the infrastructure to efficiently store, process, and manage <a href="https://www.clouddatainsights.com/data-reliability-engineering-you-cant-fly-blind-in-the-clouds/" target="_blank" rel="noreferrer noopener">vast datasets</a> needed by generative AI models to train. With the cloud, organizations can leverage data lakes, data warehouses, and data pipelines to handle the storage, organization, and processing of training data so that all training data is high enough quality and consistent enough to yield optimum results.</li>



<li><strong>Real-time Inference:</strong> While training generative AI models may benefit from the cloud&#8217;s ample resources, real-time inference often requires low latency and immediate response. Cloud-based edge computing allows organizations to deploy trained generative AI models closer to the data source, reducing latency and enabling real-time decision-making. This is particularly relevant in use cases such as real-time image or speech generation, where immediate response times are crucial.</li>
</ol>



<h3 class="wp-block-heading">Generative AI automates and optimizes cloud operations</h3>



<p>The relationship between these two technologies isn&#8217;t just one-directional. Generative AI also offers benefits because it contributes to optimized cloud operations, enhanced performance, and improved user experiences for businesses leveraging cloud technologies.</p>



<ol class="nv-cv-m wp-block-list">
<li><strong>Improved Efficiency and Automation</strong>: Companies can leverage generative AI tools to automate and optimize various aspects of cloud operations, such as resource allocation, workload management, and system optimization. AI algorithms can analyze historical data, patterns, and trends, making sense of truly large datasets to make intelligent decisions and dynamically allocate resources in the cloud. As cloud costs spiral out of control for many organizations, this level of automation and control is a welcome way to manage costs without sacrificing performance.</li>



<li><strong>Intelligent Resource Provisioning</strong>: Generative AI models help companies shift from reactive to proactive courses of action by learning from historical usage patterns to predict future resource demands. This gives businesses space and capability to proactively provision cloud resources based on predicted workloads because the necessary infrastructure is in place to handle anticipated demands, as well as prevent resource shortages and over-provisioning.</li>



<li><strong>Enhanced Security and Threat Detection</strong>: Generative AI algorithms can analyze vast amounts of log data, network traffic, and system behaviors to detect anomalies and <a href="https://www.rtinsights.com/google-combines-cybersecurity-services-into-unified-ai-platform/" target="_blank" rel="noreferrer noopener">potential security threats</a> in real time. Businesses can enhance their <a href="https://www.clouddatainsights.com/critical-steps-to-secure-your-cloud-based-apps-and-infrastructure/" target="_blank" rel="noreferrer noopener">security posture</a> by identifying and mitigating security risks, detecting intrusions, and improving incident response capabilities, ultimately safeguarding sensitive data and ensuring business continuity.</li>



<li><strong>Intelligent Monitoring and Predictive Maintenance</strong>: Generative AI can analyze system logs, performance metrics, and historical data to identify patterns and detect early signs of potential system failures or performance degradation. By leveraging generative AI for monitoring and predictive maintenance in the cloud, businesses can proactively address issues, reduce downtime, and optimize the performance and reliability of their cloud infrastructure, ensuring seamless operations and user satisfaction.</li>



<li><strong>Enhanced Service Personalization</strong>: Generative AI can analyze user behavior, preferences, and contextual data to generate personalized recommendations, content, or experiences. In cloud services, generative AI can tailor service offerings based on individual user needs, preferences, or business requirements, providing a personalized and optimized cloud experience that meets specific business use cases and drives customer satisfaction.</li>



<li><strong>Automated Troubleshooting and Issue Resolution</strong>: Generative AI models can be trained on vast repositories of troubleshooting data, system logs, and historical issue resolutions. By applying generative AI techniques, businesses can automate troubleshooting processes, predict potential issues, and even provide automated solutions or recommendations, reducing the time and effort required for issue resolution and improving overall operational efficiency.</li>
</ol>



<h3 class="wp-block-heading">What does the future hold?</h3>



<p>The future of generative AI and cloud convergence promises transformative advancements, with highly realistic and context-aware generative AI models running on scalable cloud architectures. This convergence will enable real-time, interactive, and personalized experiences across various industries. Cloud providers will continue to develop specialized platforms and services tailored for generative AI, to help companies streamline, deploy, and iterate projects using generative AI as a foundation.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/Elizabeth-Wallace-RTInsights-141x150-1.jpg" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/elizabeth-wallace/" class="vcard author" rel="author"><span class="fn">Elizabeth Wallace</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain &#8211; clearly &#8211; what it is they do.</p>
</div></div><div class="clearfix"></div></div></div>]]></content:encoded>
					
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		<post-id xmlns="com-wordpress:feed-additions:1">3322</post-id>	</item>
		<item>
		<title>Nailing AI From Cloud to the Edge</title>
		<link>https://www.clouddatainsights.com/nailing-ai-from-cloud-to-the-edge/</link>
					<comments>https://www.clouddatainsights.com/nailing-ai-from-cloud-to-the-edge/#respond</comments>
		
		<dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator>
		<pubDate>Thu, 04 May 2023 15:43:55 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[Cloud strategy]]></category>
		<category><![CDATA[edge computing]]></category>
		<category><![CDATA[Practitioner]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=2879</guid>

					<description><![CDATA[Domino Data Labs demonstrates how to build a successful workflow for deploying AI from cloud to the edge in this on-demand NVIDIA workshop.]]></description>
										<content:encoded><![CDATA[
<figure class="wp-block-image size-full"><img decoding="async" width="1000" height="553" src="https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_640661060_S.jpg" alt="" class="wp-image-2880" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_640661060_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_640661060_S-300x166.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_640661060_S-768x425.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption">Learn how MLOps platform, Domino Data Lab, builds a successful workflow to deploy AI from cloud to the edge.</figcaption></figure>



<p>We&#8217;ve all heard the dire warnings about artificial intelligence deployments and their often dismal ROI. 2023 is the year companies look to overcome lackluster results in AI and demonstrate the value of their investments. Edge deployment is one critical piece of this mission, but many companies need help to create a deployment-ready workflow. At NVIDIA&#8217;s latest GTC conference,&nbsp;<strong>Brandon Johnson</strong>, Senior Solutions Architect, Edge AI (NVIDIA); and&nbsp;<strong>Yuval Zukerman</strong>, Director, Technical Alliances (Domino Data Lab), demonstrated one such workflow.</p>



<h3 class="wp-block-heading">Why edge computing is such a significant component of these workflows</h3>



<p>Edge computing is critical for deploying artificial intelligence (AI) deep learning models in the cloud because it helps to address some of the challenges associated with running these models in a centralized cloud environment. It addresses:</p>



<ul class="nv-cv-m wp-block-list">
<li><strong>The latency caused by massive data volumes required to power AI models:</strong> Edge computing processes data locally on the device or in a nearby server rather than sending it to the cloud. This can improve the speed and responsiveness of the application and help reduce network congestion.</li>



<li><strong>Bandwidth hogging and waste:</strong> By the same token, edge computing also reduces strain on bandwidth, helping increase the efficiency of AI tools and apps.</li>



<li><strong>Security concerns specific to AI:</strong> Because deep learning models are data-hungry, much of this data is bound to be sensitive. Edge computing reduces exposure by bypassing central processing.</li>



<li><strong>Reliability of various AI models and tools:</strong> That same data hunger also requires a constant flow of input. Edge computing can help improve reliability by processing data locally on the device or in a nearby server—even if the cloud connection is lost—helping ensure that the application continues even in the event of network disruptions or other issues.</li>
</ul>



<h3 class="wp-block-heading">Why do companies struggle to build a successful edge to cloud workflow?</h3>



<p>There are many reasons companies struggle to build an edge-to-cloud workflow for their AI deployments. Some of these obstacles include:</p>



<ul class="nv-cv-d nv-cv-m wp-block-list">
<li><strong>A lack of expertise:</strong> Technology expertise is still a highly competitive field, and not all companies can afford to invest in new talent—or even attract it. Many are relying on reskilling and upskilling to fill the gap, but this takes time.</li>



<li><strong>Existing complexity and integration issues:</strong> Companies may struggle to understand how to connect edge devices to the cloud, how to manage and analyze data at scale, and how to build and deploy AI models effectively, especially if legacy systems are still an active part of the ecosystem.</li>



<li><strong>Concerns about cost:</strong> In a catch-22, AI can be both cost-saving and cost-creating, depending on the situation. Companies may struggle to justify the new cost involved with building new stacks and investing in upgraded hardware/software, particularly if they are unsure of the long-term benefits of the technology.</li>



<li><strong>Security concerns:</strong> Maintaining required security protocols across a cloud ecosystem can be challenging, leading to alert fatigue and accidental loopholes with updates. Companies may struggle to ensure that data is protected at the edge and in the cloud.&nbsp;</li>
</ul>



<p>Building a successful edge-to-cloud workflow for AI requires careful planning, investment, and expertise. Companies that can overcome the challenges associated with this technology can unlock significant benefits, including improved efficiency, better insights, and greater agility in responding to changing market conditions.&nbsp;</p>



<h3 class="wp-block-heading">How MLOps platform Domino approaches this challenge</h3>



<p>Zukerman uses the fictional company GlobalCo Chemicals to demonstrate how optimized tools can help companies overcome these barriers in Domino Data Lab&#8217;s workshop, &#8220;Deploy a Deep Learning Model from the Cloud to the Edge.&#8221; In this example, the company is investing in robotics to help ease risks associated with working on the factory floor.&nbsp;</p>



<p>GlobalCo has decided to purchase robots, allowing workers to perform tasks with a combination of voice commands and manual intervention. The models require training for the voice commands. To make this seamless, speech recognition needs to be ultra-fast—performing in milliseconds—and robots must deploy in a way that overcomes traditional inconsistencies associated with WiFi networks.</p>



<p>The fictional data science team on this project will guide the robotics voice commands. Engineers will use MATLAB to develop models and leverage the Domino platform for collaboration. In this scenario, the chosen model is a network pretrained for audio.</p>



<p>The development workflow for this project follows the MLOps model lifecycle.</p>



<p>The steps:</p>



<ol class="nv-cv-d nv-cv-m wp-block-list">
<li><strong>Data exploration:</strong> The first step in developing an AI model is exploring the data. In this case, the team uses MATLAB to explore and reshape data and convert audio files into MEL spectrograms. This step involves understanding the characteristics of the data, identifying any patterns or trends, and cleaning and preparing the data for use in the model.</li>



<li><strong>Model development and training:</strong> Once the data has been explored and prepared, the team can begin developing and training the model. In this case, the team uses a pre-trained Yamnet neural network trained on audio signals. The team uses GPU infrastructure to train the model and adjusts the structure of network layers to improve model performance. MATLAB automates pre-processing steps, training the network, and assessing the model&#8217;s effectiveness.</li>



<li><strong>Model API and container packaging:</strong> Once the model has been trained and assessed, the team can package it as a model API and container. To do this, MATLAB&#8217;s Compiler SDK generates a Python package wrapping the model, and the package is published as a Domino model API. The team creates a Python driver function to act as the Model API entry point and saves the file to the Domino file system. The model API can be accessed and used by other applications, making integrating the model into different workflows easier.</li>



<li><strong>Container registry:</strong> The container running the model is published to NVIDIA&#8217;s Fleet Command container registry. This step involves adding the container to the registry, which acts as a central repository for all containers used in the project. Using a container registry, the team can easily manage and deploy containers and ensure that all containers are up-to-date and consistent across the project.</li>



<li><strong>Model deployment to the edge:</strong> The final step in deploying an AI model is to deploy it to the edge locations connected to the platform using Fleet Command&#8217;s container registry. The team selects the location and the model from the drop-down list and deploys it. Fleet Command takes care of the rest, ensuring that the model is deployed to the correct location and can be used by other applications. This step is critical for ensuring that the model is available where needed and can be used to improve business processes and decision-making.</li>
</ol>



<h3 class="wp-block-heading">AI at the edge is not only possible but mission-critical</h3>



<p>The fictional team leveraged a unified platform to collaborate across tools and operationalize AI. They were able to abstract away the complexity that often stands in the way of companies building a cloud-to-edge workflow and were able to deploy AI more easily to edge locations, taking full advantage of the value AI can offer. </p>



<p>See the presentation and watch as they demonstrate the workflow with NVIDIA&#8217;s <a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s52424/">on-demand workshops</a>.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/Elizabeth-Wallace-RTInsights-141x150-1.jpg" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/elizabeth-wallace/" class="vcard author" rel="author"><span class="fn">Elizabeth Wallace</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain &#8211; clearly &#8211; what it is they do.</p>
</div></div><div class="clearfix"></div></div></div>]]></content:encoded>
					
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		<post-id xmlns="com-wordpress:feed-additions:1">2879</post-id>	</item>
		<item>
		<title>Observability: Key to Managing Complex Infrastructures</title>
		<link>https://www.clouddatainsights.com/observability-key-to-managing-complex-infrastructures/</link>
					<comments>https://www.clouddatainsights.com/observability-key-to-managing-complex-infrastructures/#respond</comments>
		
		<dc:creator><![CDATA[Salvatore Salamone]]></dc:creator>
		<pubDate>Thu, 29 Sep 2022 16:29:54 +0000</pubDate>
				<category><![CDATA[Cloud Strategy]]></category>
		<category><![CDATA[Governance]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[observability]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1508</guid>

					<description><![CDATA[Modern infrastructures have a greater need for network visibility, observability, and ultimately the automation of network management functions.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/09/observability-Depositphotos_202894396_s-2019-370x231-1.jpg" alt="" class="wp-image-1865" width="640" height="399" srcset="https://www.clouddatainsights.com/wp-content/uploads/2022/09/observability-Depositphotos_202894396_s-2019-370x231-1.jpg 370w, https://www.clouddatainsights.com/wp-content/uploads/2022/09/observability-Depositphotos_202894396_s-2019-370x231-1-300x187.jpg 300w" sizes="(max-width: 640px) 100vw, 640px" /><figcaption><em>Modern infrastructures have a greater need for network visibility, observability, and ultimately the automation of network management functions.</em></figcaption></figure></div>


<p>Network and corporate infrastructures are ever-so more important today as companies deploy new applications and undergo digital transformations. As such, there is a greater need for network visibility, observability, and ultimately the automation of network management functions.</p>



<p>Perhaps the biggest current change in network management is its role in aligning IT and business objectives. Network managers need real-time insights about their operations to ensure the infrastructure supports the needs of the business. To accomplish this, they need awareness and continuous monitoring. Specifically, they must monitor and collect real-time network status information of the underlying systems used to meet the business objective.</p>



<p>That information can be used to dynamically optimize network resources (using real-time status information) to ensure the network delivers the performance and availability needed for the specific business goal.</p>



<p>Currently, many organizations are transitioning from traditional reactive network management approaches to more proactive methods. An example of how the different network management techniques work is how each would ensure an executive video conference goes off without a hitch. In the succession of network management strategies:</p>



<ul class="wp-block-list"><li>The traditional approach to network management would be to wait for an angry call from executives complaining about the poor quality of their call. Next, an IT manager would use troubleshooting tools to identify the problem. And then make changes (perhaps increase the site’s bandwidth before the next call is made).</li><li>A more proactive approach would spot an increase in dropped or resent packets and other indicators of a poor video conferencing session and take corrective actions in real-time. For example, an IT manager could instruct a router or other edge device to give the video conferencing traffic more bandwidth or assign a lower priority to traffic from other users.</li><li>A more holistic approach would identify the business goal (a high-quality executive video conferencing session at 10 a.m. Monday), translate that into commands that configure the hardware to control bandwidth during the call, monitor the activity of other users and applications that are consuming great amounts of bandwidth, and dynamically adjust bandwidth to the executives while controlling bandwidth used by others.&nbsp;</li></ul>



<p><strong>See also:</strong>&nbsp;<a href="https://www.rtinsights.com/4-key-trends-in-monitoring-and-observability/" target="_blank" rel="noreferrer noopener">4 Key Trends in Monitoring and Observability</a></p>



<h3 class="wp-block-heading"><strong>Complexity requires observability and AIOps</strong></h3>



<p>Until recently, network infrastructures were relatively static. Physical boundaries separated the corporate network that contained most end-user applications, data, and services within the LAN and WAN. Thus, from a network perspective, if the network devices were up and pushing packets, relatively little added visibility was required.&nbsp;<a href="https://en.wikipedia.org/wiki/Simple_Network_Management_Protocol" target="_blank" rel="noreferrer noopener">SNMP</a>, ping, traceroute, and Syslog reporting were all that was needed.</p>



<p>The use of cloud-based resources (applications, compute power, infrastructure, and more) makes network management more challenging. Visibility gaps in network monitoring and alerting tools arise with networks now stretching into third-party managed infrastructure-as-a-service (IaaS) clouds and apps/data moving into platform-as-a-service (PaaS) and SaaS environments.</p>



<p>What’s needed is more monitoring and alerting capabilities. However, such capabilities can add to the workload of an already busy network administrator. That is why the industry is undergoing a shift away from separate network, application, and device monitoring tools to a more inclusive approach of artificial intelligence (AI) for IT operations (<a href="https://www.rtinsights.com/tag/aiops/" target="_blank" rel="noreferrer noopener">AIOps</a>).</p>



<p>Modern monitoring and observability platforms offer many of these benefits. AIOps platforms combine traditional monitoring tools with streaming telemetry. They analyze all of the data to spot anomalies, derive insights, and make predictive assessments of the state of the systems. They analyze each data source and correlate multiple anomalies to automate the identification of problems while also providing detailed information about the potential source of and problem. Thus, if a modern monitoring and observability platform, enabled by AI, is properly implemented, it provides more visibility into potential problems and eliminates many manual troubleshooting and remediation tasks.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/sal-headshot-150x150-1.webp" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/ssalamone/" class="vcard author" rel="author"><span class="fn">Salvatore Salamone</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">1508</post-id>	</item>
		<item>
		<title>Evolving Technology: AIOps, Observability and CI</title>
		<link>https://www.clouddatainsights.com/evolving-technology-aiops-observability-and-ci/</link>
					<comments>https://www.clouddatainsights.com/evolving-technology-aiops-observability-and-ci/#respond</comments>
		
		<dc:creator><![CDATA[James Connolly]]></dc:creator>
		<pubDate>Wed, 17 Aug 2022 19:30:25 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[Governance]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[continuous intelligence]]></category>
		<category><![CDATA[observability]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1507</guid>

					<description><![CDATA[Artificial intelligence and real-time analytics are driving three core technology concepts.]]></description>
										<content:encoded><![CDATA[
<p><strong>The three evolving technology concepts sometimes overlap in their roles.</strong></p>



<p>Artificial intelligence and real-time analytics are driving three core technology concepts. They are evolving technologies taking shape as sort of a three-legged stool. AIOps, observability, and continuous intelligence are real-time concepts positioned as keys to how information is processed and utilized in enterprise organizations.</p>



<p>Their proponents want enterprise leaders to think of those three amigos as being crucial to gaining insight into enterprise data, IT operations, and business performance.</p>



<p>By merging artificial intelligence (AI) with operations technologies and performance metrics, AIOps can, at minimum, take over much of the monitoring and alert infrastructure. In some cases, it resolves alerts and helps restore systems in outages. Then, observability – more of an approach rather than a technology – offers a unified view of operational systems and metrics to identify approaching issues or outages. It then offers up best practices for solving problems. Then, there’s continuous intelligence, which is casually called CI, brings the ability to learn on an ongoing basis from previous experiences as it gathers data on today’s systems performance.</p>



<h3 class="wp-block-heading">Dependent yet independent</h3>



<p>If it sounds like the three evolving technology concepts sometimes overlap in their roles, that’s true. Yet, they don’t always rely on the other concepts to work. If it sounds like all three are too futuristic for some organizations, also true. Yet, each of the three already is working for various organizations, even if that use is on a limited scale.</p>


<div class="wp-block-image">
<figure class="alignright is-resized"><img loading="lazy" decoding="async" src="https://www.rtinsights.com/wp-content/uploads/2021/09/Kevin-Petrie-Eckerson-Group.jpg" alt="" class="wp-image-42190" width="222" height="259"/><figcaption>Kevin Petrie, Eckerson Group</figcaption></figure></div>


<p><a href="https://www.eckerson.com/" target="_blank" rel="noreferrer noopener">Eckerson Group</a>&nbsp;Vice President of Research Kevin Petrie positions these evolving technologies as a Venn Diagram, with just an element of each overlapping in the middle. In a recent interview, Petrie, who has been tracking all three concepts, helped to position them and offer some thoughts on their statuses. He also highlighted where the three may be independent of each other.</p>



<p>Playing a role in the evolution of all three concepts is the ongoing shift by enterprises from legacy, on-premises data centers to cloud-first operations.</p>



<p>Suppose that someone said enterprises don’t necessarily need AIOps, which some other experts pitch as the basis for the other two tech concepts. Petrie said that the “AI” portion isn’t a hard requirement.</p>



<p>“I think AIOps if done well — it’s not always done well – is a good way to move the ball forward with ITOps in particular. You thereby manage environments or client environments more effectively to ensure application performance and so forth. But I think there are a lot of ways in which ITOps and DevOps, and CloudOps, which provides those two, can be handled very effectively without necessarily doing AIOps,” he said. “You don’t necessarily need AI or machine learning in order to optimize your cloud environment. I think a solid foundation starts with log analytics.”</p>



<h3 class="wp-block-heading">Slow adoption</h3>



<p>The state of the often-hyped AIOps is similar to that of other future technologies, according to Petrie. “Yes, it’s catching on among early adopters. But I think it’s like a lot of technologies that are getting some bleeding-edge adopters. There are some early adopters, but enterprises still have a long way to go. In general, our estimate is that a lot of the enterprises we work with are five years behind what the vendors are talking about.”</p>



<p>Enterprises may rely on AIOps tools to collect system data that observability and continuous intelligence systems rely upon. That’s an area where AIOps overlaps continuous intelligence and observability in an imaginary Venn Diagram.</p>



<p>But how to differentiate observability and continuous intelligence?</p>



<p>Petrie offered his thoughts:</p>



<p>“My definition of continuous intelligence is that it’s ingesting, transforming and analyzing real time data and historical data, internal data and external data in order to optimize a system. In my view that’s continuous intelligence. So that could be a telephone network, a location-based customer application for a mobile phone. It can be a whole host of those of things, but it’s really those elements, internal and external and data , real time data, historical data, all of which you transform and analyze to address real-time business events”.</p>



<p>Petrie said leading edge enterprises in multiple verticals are turning to continuous intelligence “to seize opportunities and reduce risk in a number of settings.” So, it could be different manufacturing companies or logistics companies that are trying to optimize mechanical equipment or networks of delivery trucks, things like that. It could be companies that are trying optimize offers to consumers through their eCommerce platforms.”</p>



<p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/the-challenge-of-full-stack-observability/" target="_blank" rel="noreferrer noopener">The Challenge of Full-Stack Observability</a></p>



<h3 class="wp-block-heading">Observability in ITOps</h3>



<p>He said&nbsp;<a href="https://www.rtinsights.com/beyond-aiops-observability-for-effective-it-operations/" target="_blank" rel="noreferrer noopener">observability is more for ITOps</a>, while continuous intelligence can extend beyond IT into other business functions.</p>



<p>“Continuous intelligence is one way to approach observability but observability in my view is fundamentally about IT systems and improving the efficiency of IT systems. Things were supposed to get a lot easier when organizations digitized and moved to the cloud and so forth, but it’s actually a lot harder, noted Petrie.</p>



<p>“Figuring out how to manage all these systems requires observability, which is based on log analytics but also understanding from a tracing perspective how the end-to-end workflow is for different events on an infrastructure.&nbsp; So, I think continuous intelligence is one way to approach and address observability, but continuous intelligence may be applied to other use cases, and observability can be tackled in different ways”, said Petrie.</p>



<h3 class="wp-block-heading">Ever-evolving technology</h3>



<p>As with any leading edge technologies, AIOps, observability, and continuous intelligence are likely to bump into each other with promises to cure all that ails IT and business. Yet, they also will be offered as partner technologies for each other, taking more defined shapes as time passes.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/08/James-Connolly-150x150-1.jpg" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/james-connolly/" class="vcard author" rel="author"><span class="fn">James Connolly</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>James Connolly is a technology journalist with deep experience as a reporter, writer, and editor. He formerly was lead editor with Informa/UBM&#8217;s InformationWeek, Network Computing, and All Analytics media sites. He has more than 30 years experience in tech publishing, including work with Computerworld, TechTarget, and MassHighTech. He has covered a wide variety of information technology sectors with a focus on how enterprise organizations implement and derive benefits from tech. He is a former news reporter for the Boston Herald.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">1507</post-id>	</item>
		<item>
		<title>Addressing Modern Cloud App Problems with Observability and AIOps</title>
		<link>https://www.clouddatainsights.com/addressing-modern-cloud-app-problems-with-observability-and-aiops/</link>
					<comments>https://www.clouddatainsights.com/addressing-modern-cloud-app-problems-with-observability-and-aiops/#respond</comments>
		
		<dc:creator><![CDATA[Salvatore Salamone]]></dc:creator>
		<pubDate>Wed, 17 Aug 2022 19:18:17 +0000</pubDate>
				<category><![CDATA[Cloud Strategy]]></category>
		<category><![CDATA[Security]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[observability]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1521</guid>

					<description><![CDATA[Increasingly, the industry is migrating from monitoring to observability and solutions that use AI to assist in managing alerts and correlating incidents.]]></description>
										<content:encoded><![CDATA[
<p>Increasingly, the industry is migrating from monitoring to observability and solutions that use AI to assist in managing alerts and correlating incidents.</p>



<p>The complexity of modern cloud applications makes it hard to detect problems in the making and find root cause issues that impact the user. Traditional monitoring and tracing solutions produce a glut of data and alerts that can overwhelm operations staff and SREs. At best, the data they produce can be used after an incident to try to find the cause of a problem.</p>



<p>Increasingly, the industry is migrating from monitoring to observability and solutions that use AI to assist in managing alerts and correlating incidents. Recently RTInsights sat down with Phil Tee, Chairman, CEO, and Co-Founder of&nbsp;<a href="https://www.moogsoft.com/">Moogsoft</a>, to talk about common problems with modern cloud applications, the shortcomings of traditional tracing and monitoring solutions in detecting them, and the need for AIOps and&nbsp;<a href="https://www.rtinsights.com/center-for-observability/">observability</a>. Here is a summary of our conversations.</p>



<p><strong>RTInsights: In modern cloud applications, what are some of the most common problems that lead to downtime, service disruptions, and poor performance?&nbsp;</strong></p>


<div class="wp-block-image">
<figure class="alignright is-resized"><img loading="lazy" decoding="async" src="https://www.rtinsights.com/wp-content/uploads/2021/11/Phil-Tee-Headshot.png" alt="" class="wp-image-43881" width="296" height="295"/><figcaption>Phil Tee, CEO and Co-Founder, Moogsoft</figcaption></figure></div>


<p><strong>Tee:</strong>&nbsp;It’s been an interesting journey going from monolithic applications to the microservices intrinsic to the design of a modern SaaS platform. It may seem to be a trite thing to say, but common problems are often due to errors – and particularly unforced errors in either the design of the microservices or new code pushes that occur that can cause these issues.</p>



<p>When you think about how a service is composed of microservices and how an application works, you’re decomposing the functionality into independent operating units that interface with each other through a defined interface. The sort of edge cases that arise are very difficult to foresee and test for.</p>



<p>For example, in times of very, very high load, you might find that there’s one microservice that just can’t keep up with other services in the application as a whole. As a result, it starts to run slow. It’ll start to build up queues on a message bus, and the whole thing will eventually collapse. That’s often the case if you haven’t really considered how the application performs under horizontal scaling.</p>



<p>Ultimately one thing that hasn’t changed is that it’s the scenarios that you do not anticipate that catch you out. In a microservice-composed SaaS application, it is often the interplay between individual microservices that is at the heart of the issue. And the key, in terms of being able to deal with that, is high-quality observability of the application as a whole.</p>



<p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/splunk-and-aws-lead-open-cybersecurity-framework-effort/" target="_blank" rel="noreferrer noopener">Splunk and AWS Lead Open Cybersecurity Framework Effort</a></p>



<p><strong>RTInsights: Why are these problems hard to detect using traditional tracing and monitoring solutions?&nbsp;</strong></p>



<p><strong>Tee:</strong>&nbsp;I characterize the level of sophistication of most tools being used in the modern observability space as straight out of the 1990s. They gather some metrics, stick a threshold on those metrics, and generate an alert when the threshold is exceeded. The world’s just not that simple anymore.</p>



<p>There’s a requirement for these tools to get much more sophisticated in their anomaly detection and correlation. This sophistication is necessary for quality correlation because of the volume of data and also the complete absence of a definitive reference model of how everything interacts. As such, from our perspective, the problem with traditional solutions is the absence of AI in operations.</p>



<p><strong>RTInsights: What’s needed to identify problems?&nbsp;</strong></p>



<p><strong>Tee:</strong>&nbsp;In most cases, tools use the equivalent of high school statistics and maybe high school AI, in the sense of linear regression or something similar. This makes Identifying problems much harder than it needs to be. What is needed are AI algorithms, correlation techniques, and next-generation anomaly detection techniques. A solution also massively benefits from the normalization of the data, so you have context. Most particularly, the tool must be able to work on data in motion to detect and identify emergent correlations that, ultimately, downstream will turn into a problem.</p>



<p><strong>RTInsights: How does Moogsoft help companies become more proactive and address problems in the making before they cause downtime?</strong></p>



<p><strong>Tee:</strong>&nbsp;You need a system that doesn’t do root cause analysis in a post-mortem mode.&nbsp; It’s about being able to evolve your understanding of the state of the system in real time, as opposed to waiting for a complete set of evidence before you deduce that the world is on fire. That is why you need AIOps. There are very few of us that do it that way. Most of the tools that claim to do it that way do not; most of them are very similar to the platforms I built in the 1990s, but perhaps with the addition of SaaS delivery. It makes me sad to contemplate people replacing Netcool with essentially Netcool in the cloud – the outcome will be the same – downtime.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/sal-headshot-150x150-1.webp" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/ssalamone/" class="vcard author" rel="author"><span class="fn">Salvatore Salamone</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">1521</post-id>	</item>
		<item>
		<title>AIOps and Observability Roll into the Next Stage</title>
		<link>https://www.clouddatainsights.com/aiops-and-observability-roll-into-the-next-stage/</link>
					<comments>https://www.clouddatainsights.com/aiops-and-observability-roll-into-the-next-stage/#respond</comments>
		
		<dc:creator><![CDATA[James Connolly]]></dc:creator>
		<pubDate>Wed, 17 Aug 2022 19:13:37 +0000</pubDate>
				<category><![CDATA[Cloud Strategy]]></category>
		<category><![CDATA[Governance]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[observability]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1520</guid>

					<description><![CDATA[Expect to see new uses for AIOps and observability as IT organization continue to adopt the concepts.]]></description>
										<content:encoded><![CDATA[
<p>Expect to see new uses for AIOps and observability as IT organization continue to adopt the concepts in 2022.</p>



<p>The year 2022 marks a time of maturation for AIOps and observability with an increasing focus on ways to use the evolving concepts.</p>



<p>Throughout the history of information technology, the adoption and implementation cycle for new tech has boiled down to three phases: If, When and How. Even Gartner’s&nbsp;<a href="https://www.gartner.com/en/research/methodologies/gartner-hype-cycle" target="_blank" rel="noreferrer noopener">Hype Cycle</a>&nbsp;can be broken down into those three stages. Will organizations and users utilize the new tech? How soon will it be useful? And, what will the tech be used for?</p>



<p>True, there’s a fourth stage – Abandonment. But by time once productive technologies are ready for the trash heap it’s original implementors don’t care. They are retired or taking the eternal dirt nap. Even the PC is already in its fifth decade!</p>



<p>Few enterprises today aren’t using or at least planning for AIOps, and observability is proving itself to be a core element of successful AIOps and the overall business strategy.</p>



<p>So, what does 2022 have in store for AIOps and observability? The consensus seems to be that implementers will take the two to the next level as valued tools that keep systems running and supporting user and customer needs.</p>



<p>Here’s a sampling of tech observers and providers expect for the year ahead.</p>



<h3 class="wp-block-heading">Hyperautomation and AIOps</h3>



<p>In a recent presentation, Douglas Toombs, research vice president at Gartner, tied hyperautomation to the future for AIOps. “As hyperautomation is a critical path to achieve growth and operational excellence, I&amp;O leaders must make automation a first-class discipline in everything they do,” Toombs said. That hyperautomation can be a key to effective AIOps and incident response automation, he said.</p>



<p>On the infrastructure and operations front — the systems that AIOps manages — Gartner predicts that organizations will turn to “Just-In-Time Infrastructure.”</p>



<p>“The speed at which infrastructure can be deployed is becoming just as important as putting the right infrastructure in the right place – colocation, data center, at the edge and more. This is the idea behind just-in-time infrastructure,” said Gartner analysts at the firm’s recent infrastructure and operations conference.</p>



<p>They added, “Borrowed from the term just-in-time manufacturing, this trend aims to reduce infrastructure deployment times as well as fuel enterprise responsiveness to business needs and anywhere operations. Gartner expects it to be a differentiating factor when enterprises compare and negotiate with service providers moving forward.”</p>



<p>Moogsoft Chief Evangelist Richard Whitehead outlined&nbsp;<a href="https://www.moogsoft.com/blog/3-aiops-areas-to-watch-in-2022/" target="_blank" rel="noreferrer noopener">three key trends</a>&nbsp;for 2022. He also cited Mordor Intelligence estimates that the AIOps market will grow from $13.5 billion in 2020 to $40.9 billion by 2026.</p>



<p>Whitehead’s predictions for 2022:<a href="https://hub.rtinsights.com/cs/c/?cta_guid=bf4f1bc4-0476-4c32-ae1b-76de2a9b4f15&amp;signature=AAH58kEHrxO-3hOe8wNEWGYiIdXnz6eSCg&amp;placement_guid=d8c10585-5de5-489f-a685-d21bd97cc30f&amp;click=d7dd4eaa-7c03-45e7-9da5-f33e3b189ab1&amp;hsutk=d1b2fe0ddec38c6312773575e95a0872&amp;canon=https%3A%2F%2Fwww.rtinsights.com%2Faiops-and-observability-roll-into-the-next-stage%2F&amp;portal_id=8019034&amp;redirect_url=APefjpFe7SgHrHqInacSz_A2yg29lBdf1Nusc9Y0aMG81RuTbWw9I-i03bwY9iLDZ-_6x2_j_WeJ6iMb0otUzHNiMUfboanH4wI2obNS4NYupL-qLPcMThk&amp;__hstc=50271033.d1b2fe0ddec38c6312773575e95a0872.1660069699598.1660069699598.1660069699598.1&amp;__hssc=50271033.1.1660069699600&amp;__hsfp=1191625704"></a></p>



<p><strong>“Trend #1: enable remote and hybrid work”</strong></p>



<p>Whitehead noted that the shift to&nbsp;<a href="https://www.rtinsights.com/the-importance-of-aiops-in-support-of-work-from-home/">work-from-home</a>&nbsp;during the pandemic has complicated data collection for IT teams. “Businesses supporting remote work sent employees home with new hardware and software, resulting in more data traffic. And IT teams, already contending with increased data production, also had to monitor streams of data with different properties.”</p>



<p>He explained how an AIOps platform’s intelligent algorithms help IT teams handle that increased and increasingly dissimilar data. &nbsp;The system looks at the aggregated data to detect patterns and predict problems before they cause disruption.</p>



<p><strong>“Trend #2: automating cybersecurity”</strong></p>



<p>Whitehead said, “AIOps, traditionally used by IT operations teams, will also help enterprise security operations teams maintain constant vigilance of their systems. AIOps uses intelligent algorithms to model the systems’ standard behavior patterns and set baselines for system performance. These platforms unlock the ability to proactively detect a cyberattack by identifying deviations in real-time and determining if a performance issue is due to a cyberattack rather than another IT issue.”</p>



<p>When security issues do arise, the same AIOps platform can identify the resources or people needed to remediate the problem.</p>



<p><strong>“Trend #3: decrease MTTR with observability”</strong></p>



<p>Whitehead said the customer fallout from Facebook’s October outage is an example why faster mean time to repair is critical to keeping users happy and productive. “While technology can’t yet provide 100% protection against service failures, a platform that combines the alert data associated with AIOps and the telemetry data associated with observability can mitigate the damage. When issues occur and every moment counts, AIOps helps SRE and DevOps teams quickly detect the incident and provide actionable insights to help resolve it.”</p>



<p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/the-future-of-aiops-this-year-and-beyond/" target="_blank" rel="noreferrer noopener">The Future of AIOps This Year and Beyond</a></p>



<h3 class="wp-block-heading">The bots step up</h3>



<p>In other predictions, authors at India’s&nbsp;<a rel="noreferrer noopener" href="https://www.analyticsinsight.net/aiops-learn-upcoming-aiops-trends-to-drive-business-revenue-in-2022/" target="_blank">Analytics Insight</a>&nbsp;foresee new levels of virtualization and new uses for chatbots.</p>



<ul class="wp-block-list"><li><strong>Virtualized Operations and Service Management:&nbsp;</strong>Throughout the Covid-19 pandemic, the virtualized way of communication has been the new normal. AIOps solutions will ensure the smooth functioning of the business operations.</li><li><strong>Chat-bots:</strong>&nbsp;Chat-bots and virtual assistants provide automated support, reducing the need for live customer service agents. AIOps can be used to improve employee productivity as machines will provide them solutions to all their queries.</li></ul>



<p>IT operations aren’t about just keeping machines running. It’s about making better use of the data generated by those machines and the people who use them. That’s not a new idea, but 2022 should see more organizations making that data a real tool of productivity.</p>



<p><a rel="noreferrer noopener" href="https://tdwi.org/articles/2021/12/15/arch-all-databases-data-mesh-open-source-communities-in-2022.aspx" target="_blank">TDWI</a>&nbsp;predicts, “Machine learning enables predictive database indexing, analytics, and more.”</p>



<p>Organizations that have utilized machine learning and predictive analytics to anticipate issues such as network outages can apply those same concepts to the database itself.</p>



<p>According to TDWI, the complexity of the enterprise database has made it difficult for human administrators to manage storage and understand factors such as data usage patterns. “In contrast, ML-powered solutions can actually create data indexes, perform reindexing, and manage storage using predictive models able to guess where data sits.”</p>



<p>TDWI also predicted, “Where data and analytics vendors steer solutions away from open source licensing, communities will steer them back.”</p>



<p>TDWI said, “It’s crucial for organizations to remain aware of any licensing changes affecting solutions they rely upon for data technologies, as well as any open source options that become available to them due to this trend. If communities do prove their power to bring solutions back into the open source fold, it will discourage future licensing shenanigans and further ensure that valuable features remain available to everyone.”</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/08/James-Connolly-150x150-1.jpg" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/james-connolly/" class="vcard author" rel="author"><span class="fn">James Connolly</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>James Connolly is a technology journalist with deep experience as a reporter, writer, and editor. He formerly was lead editor with Informa/UBM&#8217;s InformationWeek, Network Computing, and All Analytics media sites. He has more than 30 years experience in tech publishing, including work with Computerworld, TechTarget, and MassHighTech. He has covered a wide variety of information technology sectors with a focus on how enterprise organizations implement and derive benefits from tech. He is a former news reporter for the Boston Herald.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">1520</post-id>	</item>
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		<title>The Future of AIOps This Year and Beyond</title>
		<link>https://www.clouddatainsights.com/the-future-of-aiops-this-year-and-beyond/</link>
					<comments>https://www.clouddatainsights.com/the-future-of-aiops-this-year-and-beyond/#respond</comments>
		
		<dc:creator><![CDATA[David Curry]]></dc:creator>
		<pubDate>Fri, 12 Aug 2022 02:40:12 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[Security]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[monitoring]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1516</guid>

					<description><![CDATA[AIOps has seen critical gains in the past two years, as more organizations have adopted it and some are starting to mature in their use of the technology.]]></description>
										<content:encoded><![CDATA[
<p>AIOps has seen critical gains in the past two years, as more organizations have adopted it and some are starting to mature in their use of the technology.</p>



<p>Artificial intelligence for IT operations has seen a growing amount of interest in the past few years. In one&nbsp;<a href="https://www.masergy.com/press-release/artificial-intelligence-key-to-business-continuity-and-security-finds-new-masergy-state-of-aiops-study">survey</a>&nbsp;of IT leaders, 94 percent said AIops adoption was “very important” for managing networks and cloud services.&nbsp;</p>



<p>Market research firm Gartner&nbsp;<a href="https://www.gartner.com/doc/reprints?id=1-25RAD0H3&amp;ct=210407&amp;st=sb">estimated</a>&nbsp;AIOps market size will increase by 15 percent annually through to 2025, rising to $3.4 billion market value in 2025.&nbsp;</p>



<p><strong>See Also:&nbsp;</strong><a href="https://www.clouddatainsights.com/increased-it-complexity-drives-the-need-for-aiops/" target="_blank" rel="noreferrer noopener">Increased IT Complexity Drives the Need for AIOps</a></p>



<p>Even though some modern digital businesses are embracing the technology, there is still room for lots of growth in the industry from businesses unaware of the technology and those that have yet to implement a cohesive AI deployment strategy.&nbsp;<a href="https://hub.rtinsights.com/cs/c/?cta_guid=4bfb739d-cc2d-4663-b1b9-6926bccd934a&amp;signature=AAH58kHn6hI5P2oE6mnpw_uSD15m8kNypg&amp;placement_guid=45d46bee-87f8-4032-9394-2a15261ecab8&amp;click=032d0ec2-0b92-47ff-9e7a-e38d17ad2cc2&amp;hsutk=d1b2fe0ddec38c6312773575e95a0872&amp;canon=https%3A%2F%2Fwww.rtinsights.com%2Ffuture-of-aiops-2022-beyond%2F&amp;portal_id=8019034&amp;redirect_url=APefjpHAe_mZMfUvlV1OgbXOx91N2iveb-2E7yq_Uawx68f8ACTJ2wwUxigR_HUbTi_ihEvQV_JHDbuP9ijUfW1vc7hd9Wu6CrpzhbBDE2fEsB68igXqp4QiE1vLRl_HSVEEw5EkdK3ar2kTSGY70SrQutY_BatYMw&amp;__hstc=50271033.d1b2fe0ddec38c6312773575e95a0872.1660069689925.1660069689925.1660069689925.1&amp;__hssc=50271033.1.1660069689926&amp;__hsfp=1191625704"></a></p>



<p>“Enterprises have started adopting AIOps platforms to compete with and replace some traditional monitoring tool categories,” said lead Gartner analyst Pankaj Prasad. “For example, monitoring IaaS and observability is being done entirely within AIOps platforms, especially if the enterprise has its entire IT footprint in the cloud.”</p>



<p>AIOps can be split into three key IT operations:&nbsp;</p>



<ul class="wp-block-list"><li><strong>Observe (Monitoring)</strong>&nbsp;– AIOps combines historic and real-time data, previously siloed in separate applications which wouldn’t speak to each other, to provide event metrics, traces and topology. Through this, organizations are able to better contextualize events, detect anomalies and receive data and performance analysis.&nbsp;</li><li><strong>Engage (IT Service Management)&nbsp;</strong>– Through the implementation, organizations are able to engage with incidents and changes, through the use of task automation, risk analysis and knowledge management.&nbsp;</li><li><strong>Act (Automation)&nbsp;</strong>– AIOps supports a wealth of automation tools, such as scripts, runbooks and application release automations (ARAs), to help organizations automate more business processes.&nbsp;</li></ul>



<p>The combined service enables organizations to know more about IT operations through the use of real-time data streams, while also providing management and automation tools to speed up processes inside the organization.&nbsp;</p>



<p>Even some of the top-end tech organizations are still coming to terms with AIOps, and Prasad sees a point in the near future where “end-users [mature] to a point where they are ready to ask questions about what’s happening in the analytics layer, how the process is working and how the outcomes can be better.”</p>



<p>A broader push by the industry to standardize AIOps as the sole monitoring tool, instead of having several monitoring tools overlapping, is&nbsp;<a href="https://www.moogsoft.com/blog/aiops-in-2022-and-beyond-a-conversation-with-gartner/">expected</a>&nbsp;by Moogsoft CTO Richard Whitehead.&nbsp;</p>



<p>This could be led by organizations adding a senior vice president of AIOps, which some mature businesses have already done. Adding this layer of executive branch may push the industry category ahead of other monitoring and automation tools in the tech stack.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/curry-150x150-1.webp" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/david-curry/" class="vcard author" rel="author"><span class="fn">David Curry</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><div class="author-info">
<div class="author-description">
<p>David is a technology writer with several years experience covering all aspects of IoT, from technology to networks to security.</p>
</div>
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		<post-id xmlns="com-wordpress:feed-additions:1">1516</post-id>	</item>
		<item>
		<title>Increased IT Complexity Drives the Need for AIOps</title>
		<link>https://www.clouddatainsights.com/increased-it-complexity-drives-the-need-for-aiops/</link>
					<comments>https://www.clouddatainsights.com/increased-it-complexity-drives-the-need-for-aiops/#respond</comments>
		
		<dc:creator><![CDATA[Salvatore Salamone]]></dc:creator>
		<pubDate>Fri, 12 Aug 2022 02:17:50 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[Governance]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[monitoring]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1512</guid>

					<description><![CDATA[As the AIOps market matures, many in the industry believe businesses will turn to AIOps platforms as their sole monitoring tool as these platforms are enabled to natively acquire data and analyze it.]]></description>
										<content:encoded><![CDATA[
<p><strong>As the AIOps market matures, many in the industry believe businesses will turn to AIOps platforms as their sole monitoring tool as these platforms are enabled to natively acquire data and analyze it.</strong></p>



<p>Modern digital businesses need AIOps tools to enable continuous insights across an IT stack. Such insights are increasingly important as the systems that need monitoring and management become more complex, more distributed, and more out of the tight control afforded when everything was on-premises.</p>



<p>In particular, the use of cloud-based resources makes network management more challenging. Visibility gaps in network monitoring and alerting tools arise with networks now stretching into third-party managed infrastructure-as-a-service (IaaS) clouds and apps/data moving into platform-as-a-service (PaaS) and SaaS environments.</p>



<p>While more monitoring and alerting capabilities are great, they can add to the workload of an already busy IT staff. That is why the industry is undergoing a shift away from separate network, application, and device monitoring tools towards what is being referred to as artificial intelligence (AI) for IT operations, or AIOps for short.</p>



<p>AIOps platforms combine traditional monitoring tools with streaming telemetry and analyze all of it using AI. AI analyzes each data source and correlates multiple anomalies to automate the identification of problems while also providing detailed information on how to fix the issue. Thus, if an AIOps platform is properly implemented, not only does it provide more visibility into potential problems, but it also eliminates many manual troubleshooting and remediation tasks.</p>



<p>An AIOps solution should automatically discover the relationships between status data and the business outcome. (Under a rules-based system, the same amount of setup work is needed as in many manual systems.)</p>



<p>There is also a difference between monitoring and management. A tool should provide insights instead of the human user looking at data and then sorting out what is going on. The tool should tell an IT manager that there is something that needs attention. The goal: AIOps provides the automation to reduce the time spent manually intervening and allow for more time with applications.</p>



<p><strong>See also: </strong><a href="https://www.clouddatainsights.com/migrating-your-databases-to-the-cloud-consider-your-options/" target="_blank" rel="noreferrer noopener">Migrating Your Databases to the Cloud? Consider Your Options</a></p>



<h3 class="wp-block-heading">Gartner’s view</h3>



<p>Such systems provide insights that tell the full story of what’s happening behind systems, allowing IT teams to achieve the operational efficiencies and high availability that lead to customer satisfaction.</p>



<p>This was the theme of a recent strategic advisory session, “Gartner’s Vision for AIOps in 2022 and Beyond,” presented by Lead Gartner Analyst Pankaj Prasad. According to&nbsp;<a href="https://www.moogsoft.com/blog/aiops-in-2022-and-beyond-a-conversation-with-gartner/" target="_blank" rel="noreferrer noopener">a recent blog</a>&nbsp;by Richard Whitehead, Chief Evangelist at Moogsoft, Prasad discussed the importance of AIOps as companies continue to adopt new technologies.</p>



<p>“Connecting the dots to convey a story is where we see a lot of organizations lean towards the AIOps platform,” according to Prasad. To that point, the two noted that AIOps cuts across the three domains of IT Operations:</p>



<ul class="wp-block-list"><li><strong>Observe (Monitoring):</strong>&nbsp;<em>Do we know what’s happening?</em>&nbsp;AIOps offers real-time and historical data by analyzing events metrics, traces, and topology and administers data analysis, anomaly detection, performance analysis and correlation, and contextualization.</li><li><strong>Engage (IT Service Management):</strong>&nbsp;<em>What’s happening within IT, and how does it relate to the end-user?&nbsp;</em>AIOps provides notifications about incidents, dependencies, and changes and covers task automation, change risk analysis, SD Agency performance analysis, and knowledge management.</li><li><strong>Act (Automation):</strong>&nbsp;<em>What are we doing within the IT Operations space?&nbsp;</em>AIOps supports scripts, runbooks, and application release automations (ARAs).”</li></ul>



<p><strong>See also:&nbsp;</strong><a href="https://www.rtinsights.com/aiops-is-an-essential-devops-toolchain-component/" target="_blank" rel="noreferrer noopener">AIOps is An Essential DevOps Toolchain Component</a></p>



<h3 class="wp-block-heading">AIOps futures</h3>



<p>As the market matures,&nbsp;many in the industry believe businesses will turn to AIOps platforms as their sole monitoring tool as these platforms are enabled to natively acquire data and analyze it. This makes it ideal for many aspects of business operations and useful to many groups, including IT, operations, site reliability engineers, and even SecOps.</p>



<p>As such, the market is poised for large growth. Last year,&nbsp;<a href="https://www.gartner.com/doc/reprints?id=1-25RAD0H3&amp;ct=210407&amp;st=sb" target="_blank" rel="noreferrer noopener">Gartner said</a>&nbsp;that AIOps in the IT operations management market will grow at a compound annual growth rate of 15% per year through 2025 from more than $1 billion in 2020.</p>



<p>Part of the allure of AIOps is that it helps address problems as complexity grows. Companies routinely use multiple clouds and a mix of multiple cloud services and legacy systems. It provides insight into the state of those systems.</p>



<p>Additionally, most companies must now support many more devices and more applications. IT staff often cannot keep up with the large number of alerts, logs, telemetry data, and more. As such, they are looking to AIOps to help manage IT operations and security, relying on AI and machine learning to help sort through the data.</p>



<p>The promise of AIOps isn’t just to help IT teams respond to outages and performance problems. Perhaps the greatest value is in using predictive analytics to identify and prevent upcoming failures. And increasingly, AIOps is being adopted by developers. Specifically, DevOps teams are starting to shift its use to earlier in the pipeline to analyze development and pre-production environments and reduce risk.<a href="https://hub.rtinsights.com/cs/c/?cta_guid=f18018a9-7cd8-4c07-b799-f2e324d405df&amp;signature=AAH58kF2i-IwkEupivjwEjToWLHWOthGbQ&amp;placement_guid=d0413c26-95d1-4dc7-a112-192c3e372de0&amp;click=6b47aa22-df54-4b3c-ba44-db0af992888b&amp;hsutk=d1b2fe0ddec38c6312773575e95a0872&amp;canon=https%3A%2F%2Fwww.rtinsights.com%2Fincreased-it-complexity-drives-the-need-for-aiops%2F&amp;portal_id=8019034&amp;redirect_url=APefjpGte1YmnhrOr_iByueDgsCWbiQpdfzDmNwNHz-f7n-POZoNBZAtY-BGARQa105lVoJMftUZBrtGye9xnaetJ0tN8J9dsiOnJ3Pa0a1yRoHbNj5lTmDgBPtZePLShGSLkLszmE6JHDEuqI-RuWjHm-hs4y2MMvTFEly886L_YFOq1DKLi4u5ZQKUMmvZuvqtl-UqfNIGK9NweG97YZ8VVu2FTbsqG5NThoQ555xAyAOS6eR2cxlI8wVe6fJbcWcTtEdi8NdY&amp;__hstc=50271033.d1b2fe0ddec38c6312773575e95a0872.1660069684953.1660069684953.1660069684953.1&amp;__hssc=50271033.1.1660069684954&amp;__hsfp=1191625704"><br></a></p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/sal-headshot-150x150-1.webp" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/ssalamone/" class="vcard author" rel="author"><span class="fn">Salvatore Salamone</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.</p>
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		<title>Why Continuous Availability Matters for Cloud Adoption</title>
		<link>https://www.clouddatainsights.com/why-continuous-availability-matters-for-cloud-adoption/</link>
					<comments>https://www.clouddatainsights.com/why-continuous-availability-matters-for-cloud-adoption/#respond</comments>
		
		<dc:creator><![CDATA[Salvatore Salamone]]></dc:creator>
		<pubDate>Fri, 12 Aug 2022 02:10:15 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[AIOps]]></category>
		<category><![CDATA[continuous intelligence]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=1511</guid>

					<description><![CDATA[Continuous availability and optimized performance are essential today. One way to ensure both is through the use of observability complemented with AIOps.]]></description>
										<content:encoded><![CDATA[
<p>Continuous availability and optimized performance are essential today. One way to ensure both is through the use of observability complemented with AIOps.</p>



<p>Nearly all companies are undertaking some form of cloud adoption these days. Efforts range from moving an app or workload to a cloud compute platform for the first time, melding cloud and on-premises activities into a hybrid cloud platform, or embracing cloud-native application architectures based on microservices and APIs. In all these variants, traditional tools that helped organizations ensure the performance and availability of their services and apps fail. Increasingly what’s needed are more modern tools that offer better observability and insights into what’s happening and an AI-based assist to help ensure continuous availability and stellar performance.</p>



<p>There are several issues at hand driving the need for modern tools. First, there is the increased complexity of cloud environments upon which organizations deploy applications and run their workloads.</p>



<p>Even a simple application such as providing a mobile front-end to a user’s account would involve backend elements maintained by the organization, a database on a public cloud, connectivity via the user’s provider, and any one of the major mobile operating systems. There are many inter-dependencies between the various elements, and the business has little control over most elements that could impact performance or availability. When a problem happens, it can take a great amount of time to determine the source of the outage. Modern observability tools using AIOps can help automate the root-cause analysis, accelerating the meantime to repair (MTTR) for an outage or other problem. This can significantly reduce the meantime to repair/recover.</p>



<p>Second, organizations can no longer be reactive, acting after a problem occurs. The traditional approach to IT management has been to wait for an angry call from customers or internal users about a service disruption or the poor quality of a service. AIOps offers a more predictive mode of operation. It enables a proactive approach that could spot, for example, an increase in dropped or re-sent packets and other indicators of poor performance and take corrective actions in real time.</p>



<p>Third, security is much more challenging when applications and services are delivered using multiple cloud elements, some of which are not under the control of an organization. With a modern observability tool, a security team could use AIOps to spot anomalies that are pre-cursors to an attack or activities that are indicative of a data breach. For example, AIOps might be used to alert the security team that an unusually large about of data is being sent out of the organization via a normally lightly used port.</p>



<p><strong>See also:</strong>&nbsp;<a href="https://www.rtinsights.com/the-role-of-aiops-in-continuous-availability/" target="_blank" rel="noreferrer noopener">The Role of AIOps in Continuous Availability</a></p>



<h3 class="wp-block-heading"><strong>Continuous availability is critical to meeting end-user expectations</strong></h3>



<p>Application performance and availability have always been important for any organization. Employees have certain expectations that the apps and services they need to get their job done will be available whenever they need them and they will perform well.</p>



<p>Similarly, any customer-facing application or service these days face even harsher user expectations. With people used to getting anything and everything instantly whenever they want it, there is very little intolerance for offerings that are unavailable or that have poor performance.</p>



<p>Numerous studies have quantified the impact any issues can have on the bottom line. Forty percent of users will&nbsp;<a href="https://www.websitebuilderexpert.com/building-websites/website-load-time-statistics/" target="_blank" rel="noreferrer noopener">abandon a website</a>&nbsp;that takes longer than three seconds to load. And 53 percent of users will&nbsp;<a href="https://www.marketingdive.com/news/google-53-of-mobile-users-abandon-sites-that-take-over-3-seconds-to-load/426070/" target="_blank" rel="noreferrer noopener">abandon a mobile app</a>&nbsp;that fails to load in three seconds.</p>



<p>If either (a website or mobile app) is unavailable or poorly performing, users will abandon the site or app. That translates into lost revenues. For example, a customer shopping online will simply jump to another merchant’s site to place that one-time order. If the customer has a good experience on that site, they may never return. So, it is not just the loss of that one purchase. It could mean the loss of a customer for life.</p>



<p>Comparably, slow performance drives ways business. A classic&nbsp;<a href="https://www.marketingdive.com/news/google-53-of-mobile-users-abandon-sites-that-take-over-3-seconds-to-load/426070/" target="_blank" rel="noreferrer noopener">Google analysis</a>&nbsp;of the issue found that 53% of users abandon sites that take more than 3 seconds to load. In fact, website and mobile app performance are so important, Google now factors both into SEO rankings. That, again, can have severe revenue implications. Imagine being dropped from a Google ranking of second on the page to off the first page of search results. A company will never be seen when customers look for its products or services.</p>



<h3 class="wp-block-heading"><strong>A needed tool for modern business</strong></h3>



<p>Continuous availability and optimized performance are essential today. One way to ensure both is through the use of observability complemented with AIOps, an essential layer for any digital organization that needs to be operational 24/7 when operating in a cloud environment.&nbsp;</p>



<p>AIOps is the deployment of machine learning to track data from sensors, traces, logs, and other sources to prevent internal and external disruption, whether that be through event correlation or anomaly detection. It can also provide better analysis of why an event happened through casualty determination.</p>



<p>Advanced AIOps platforms converge all data — metrics, traces, logs, changes, and events — for rapid, accurate reporting and analysis. Unlike old, rules-based technologies, this method can operate on partial evidence and detect problems before they become critical. AIOps also uses machine learning to dissect incidents, understanding how to catch problems earlier in the incident lifecycle, and identifying patterns that drive continuous availability.</p>



<p>Given the complexity of the average cloud-based digital organization in 2022, with layers of microservices and ephemeral architectures, AIOps is vital to an effort that seeks to ensure apps and services are available and performing well.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/sal-headshot-150x150-1.webp" width="100"  height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/ssalamone/" class="vcard author" rel="author"><span class="fn">Salvatore Salamone</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.</p>
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