[et_pb_section fb_built=”1″ admin_label=”Section” _builder_version=”4.22.0″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” locked=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.27.0″ _module_preset=”default” custom_padding=”50px||||false|false” custom_css_main_element=”z-index:0!important;” global_colors_info=”{}”][et_pb_column type=”1_4″ disabled_on=”on|on|off” _builder_version=”4.22.0″ _module_preset=”default” custom_padding=”||||false|false” sticky_position=”top” sticky_offset_top=”100px” sticky_limit_bottom=”section” motion_trigger_start=”top” global_colors_info=”{}”][et_pb_text admin_label=”indice” _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”||0px||false|false” custom_padding=”||14px||false|false” link_option_url=”#1″ global_colors_info=”{}”]<\/p>\n
Sections<\/strong><\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.27.0″ _module_preset=”default” custom_css_main_element=”z-index:0!important;” global_colors_info=”{}”][et_pb_text admin_label=”seccion” module_id=”1″ module_class=”ittopicscontent” _builder_version=”4.27.4″ _module_preset=”default” z_index=”0″ custom_margin=”0px||0px||true|false” custom_padding=”0px||0px||false|false” custom_css_main_element=”font-family:%22Pandora-Light%22;” locked=”off” global_colors_info=”{}”]As a child, I had a VHS recording of Hamburger Hill, a Vietnam war film, and one of my frustrations was that the protagonists never saw the enemy. It was all jungle and, suddenly, the jungle started shooting. Just like in IT, where you constantly monitor every corner, everything seems fine until, suddenly, there\u2019s a failure, an attack, or a disaster you didn\u2019t see coming. To help us in that task and identify the enemy lurking in the jungle, we have deep learning applied to monitoring and ITSM<\/strong>.<\/p>\n With it, we can uncover patterns invisible to the human eye<\/strong> and react earlier, predict what\u2019s about to happen, and generally save ourselves time, trouble, and defeats in our daily battle.<\/p>\n Aware that the most dangerous enemy is the one you can’t see, hidden in the thicket of silicon and bits, Pandora FMS<\/a> and Pandora ITSM<\/a> use deep learning<\/a> techniques and other forms of artificial intelligence<\/a><\/strong> to detect anomalies, correlate complex events, and generally anticipate potential ambushes.<\/p>\n If we\u2019re not using it daily as IT managers, CISOs or technology leaders, we\u2019re like Sergeant Frantz\u2019s platoon, looking everywhere but missing most of the threats.<\/p>\n That\u2019s why we will analyze what deep learning is in IT and how Pandora FMS and Pandora ITSM use it in practice<\/strong>, to provide the most scarce asset in this job: peace of mind (and management approval for a job well done).<\/p>\n Deep learning is a branch of the artificial intelligence tree and the natural evolution of machine learning. It\u2019s the T-1000 to the T-800 from Terminator 2, although as we\u2019ll see, and just like in the movie, the T-1000 is not always the best fit for every scenario.<\/p>\n Deep learning works through deep neural networks<\/strong> that learn feature hierarchies: first simple patterns, then complex combinations, and finally abstract system representations.<\/p>\n The key is that, unlike traditional models, the model itself decides which signals matter<\/strong>, hence the deeper learning that reduces the need for hand-crafted rules.<\/p>\n This enables training of multimodal models that combine metrics, ticket text, network traces, and conversations… A single model can integrate heterogeneous data such as CPU history, service relationships, or natural language associated with incidents<\/a>.<\/p>\n Using a combination of stacked layer reviewing, activation and loss functions, or weights, this makes it possible in practice to find hidden patterns that are undetectable by traditional methods<\/strong> or human perception.<\/p>\n Within deep learning, various specialized architectures coexist:<\/p>\n The key distinction is that traditional machine learning requires a specialist to decide which metrics are important, which becomes unmanageable when dealing with thousands of variables instead of just dozens. Another major difference is that some traditional machine learning algorithms, like basic decision trees, struggle with large datasets, whereas a deep learning model improves as it is fed more data. Of course, only if that data is high quality<\/strong>, a key challenge we will address in more depth when discussing this technology\u2019s limitations.<\/p>\n Does this mean machine learning is obsolete?<\/strong> All of this sounds great, but the question is: how does it help us in the daily chaos of IT management? Classical detection relies on rigid thresholds, but deep learning builds dynamic profiles<\/strong> of what is normal in your context. Sequential models learn the lifecycle of disks, fans, CPUs, or logical services. By detecting subtle degradations (like slight temperature increases or IOPS drops), they predict possible failure windows<\/strong>. This is the key strength of Pandora SIEM<\/a>. It not only collects and displays millions of data points, but also connects the dots to present the full picture<\/strong>. Deep learning multiplies our IT management capabilities with features such as:<\/p>\n Like in a hospital emergency room, the first thing is triage to categorize and prioritize tickets as soon as possible. The Pandora ITSM chatbot combines language models with operational context<\/strong>. This enables user interaction or support for human operators in case of doubts or complex situations.<\/p>\n The end-user experience also depends on how they feel when interacting with support. Models based on BERT can process comments, surveys, and chats<\/strong> to identify trends.<\/p>\n Let\u2019s keep hammering home the usefulness of deep learning in IT with practical cases where this technology improves efficiency, focusing on our own Skynet, Pandora FMS.<\/p>\n A managed service provider collects two years of performance metrics and service outages. That\u2019s a gold mine, but too deep for models like simple regressions or pure human analysis. If we move from pure management to security monitoring<\/a>, deep learning\u2019s power lies not only in big data processing but in its ability to detect sophisticated threats<\/strong>. Imagine an e-commerce company that wants to optimize both the cost of its cloud provider and prevent outages due to saturation, which would hurt sales and customer trust. We\u2019ve already seen the benefits, and the key advantages over traditional monitoring can be summarized as:<\/p>\n But of course, nothing is perfect, and deep learning still presents significant challenges, such as:<\/p>\n Before we continue, it’s worth clarifying that Pandora FMS does not rely exclusively on deep learning<\/strong> to monitor everything from a single Iron Throne. As we\u2019ve discussed, more traditional techniques can be just as useful \u2014 or even more so \u2014 depending on the scenario.<\/p>\n That\u2019s why Pandora FMS also includes features such as capacity planning, which performs a forecast based on the time window specified by the user and assuming a more or less linear behavior<\/strong> of the target module.<\/p>\n But when it comes to IT management optimization through anomaly detection powered by AI, Pandora uses its MADE engine<\/strong>.<\/p>\n The large datasets required to train it are obtained from the Pandora FMS database, and MADE keeps a local copy of this data on disk to perform retraining and resampling tasks using the feather format, which is designed for efficient data storage.<\/p>\n In this way, we have the best of both worlds to ensure we use the most appropriate tool<\/strong> in every situation.<\/p>\n In addition, Pandora ITSM integrates language models into its support functions<\/strong>, capable of responding to user queries in natural language, suggesting articles, and documenting every intervention.<\/p>\n Likewise, it adapts to the operational style of each organization, offering the ability to create workflow rules related to ticket handling and support processes.<\/p>\n Everyone has an opinion on artificial intelligence \u2014 and so do I. But the reality is that, regardless of how the game or its players evolve, it\u2019s here to stay and it changes everything<\/strong>.<\/p>\n Not using deep learning in our IT management puts us at a disadvantage compared to competitors who do, especially when facing increasingly complex infrastructures and key data volumes that are unmanageable by traditional means. Being a Luddite may seem appealing at times, but it will only create unnecessary problems in our work.<\/p>\n Not only that, against increasingly sophisticated malicious actors who leverage this and other AI technologies for ever more complex attacks<\/strong>, going into battle without the power of deep learning is like wielding a wooden sword.<\/p>\n Looking ahead, we will likely see a convergence between deep learning, generative AI<\/a> (which is based on deep learning), AIOps with operations automation, and RPA<\/a> (Robotic Process Automation).<\/p>\n Models will not only detect anomalies but also suggest solutions, validate changes, and execute scripts<\/strong> in hybrid environments where machines and humans work together. Like Cyberpunk \u2014 but without implants and stripped of everything appealing.<\/p>\n No one truly knows what this future will look like, though many claim to. At Pandora FMS, we have always believed that deep learning equips IT managers with a lightsaber in place of the old wooden sword.<\/p>\n That said, deep learning is not a silver bullet<\/strong>, nor a master key that unlocks every door, as some claim. But it is the key to moving from reactive monitoring to proactive<\/strong> and more efficient management<\/a>.<\/p>\n Instead of putting out fires \u2014 which defines the daily grind of IT \u2014 the goal is to prevent them through anomaly detection, failure prediction, and support automation, all made possible with Pandora’s tools.<\/p>\n Now, I must find that VHS tape in the attic\u2026<\/p>\n Let\u2019s summarize the key questions about deep learning we\u2019ve covered.<\/p>\n It\u2019s an approach based on deep neural networks that learn complex representations to detect anomalies<\/strong>, forecast demand, or automate IT management decisions. Greater representation capacity, automatic learning of features and infrastructure behavior, as well as improved performance with large data volumes and workloads.<\/p>\n By learning the normal behavior of each service, identifying minimal deviations, and prioritizing alerts based on probable impact.\n
What is deep learning and how does it work?<\/h2>\n
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How does deep learning differ from traditional machine learning?<\/h2>\n
\nDeep learning, by contrast, automates the discovery of what matters and enhances representational capacity<\/strong>, identifying nonlinear relationships between metrics, tickets, and logs that were not explicitly defined.
\nIn short, it can detect things in the IT jungle that even expert eyes cannot see. <\/p>\n
\nNot at all\u2014just as Schwarzenegger (yes, I copied and pasted the name, like everyone else) proved against the T-1000.
\nSimple models are still ideal for quick diagnostics or when there is no historical data. That\u2019s why a successful strategy is often hybrid: <\/p>\n\n
What are the applications of deep learning in IT monitoring?<\/h2>\n
\nTo avoid ending up like the squad in Hamburger Hill, we can rely on:<\/p>\nAnomaly detection in performance metrics<\/h3>\n
\nThis allows more flexibility and accuracy<\/strong>. As a result, Pandora FMS uses these techniques to alert more precisely<\/strong>, reducing noise and preventing nervous breakdowns.<\/p>\nFailure prediction and proactive maintenance<\/h3>\n
\nPandora FMS integrates these forecasts. The key is not just having a brain like Dr. Xavier from X-Men to detect the right signal, but also the ability to forecast the most likely future<\/strong>, like a more accurate Nostradamus.<\/p>\nAdvanced event correlation in SIEM environments<\/h3>\n
\nThat is, it transforms information into actionable knowledge<\/strong>, identifying patterns and complex enemy ambushes. And yes, it also reduces false positives, which is great for our industry’s collective heart health.<\/p>\nHow does deep learning enhance ITSM and support automation?<\/h2>\n
Automatic ticket classification using AI<\/h3>\n
\nHere, the use of transformers allows understanding the ticket\u2019s semantics<\/strong> (which is usually \u201csomething broke\u201d and \u201cI didn\u2019t touch anything,\u201d according to the user), assigning priorities and agents based on that interpretation.<\/p>\nChatbots for interaction and management<\/h3>\n
Sentiment analysis and service quality<\/h3>\n
Real-world use cases of deep learning applied to IT management<\/h2>\n
Use case in IT management: Predicting failures in servers and networks<\/h3>\n
\nPandora FMS can integrate and correlate it, extracting the gold from that mine<\/strong> by discovering patterns that help predict potential outages and alerting before they occur<\/strong>.<\/p>\nSecurity use case: Event correlation with Pandora FMS<\/h3>\n
\nPandora learns the \u201cnormality\u201d of the monitored organization and, aware of the latest attack techniques<\/a>, can detect events that individually don\u2019t seem suspicious.
\nFor example, suppose a user accesses systems at odd \u2014but not impossible\u2014 hours, then connects to seemingly harmless servers like Google Drive. Pandora sees all of this like Sauron with his Palantir and keeps watching. Then, that user runs a PowerShell script, which deviates from organizational norms and, combined with the previous signs, smells like a threat.
\nIn this scenario, Pandora FMS can correlate these isolated \u201charmless\u201d events<\/strong> and suspect a potential data exfiltration attempt and act accordingly<\/strong>.
\nIt sends an alert to the SOC and can even connect to a SOAR<\/a> to trigger automated countermeasures like cutting the connection and blocking the user until the SOC investigates.<\/p>\nIT management use case: Optimizing cloud service scaling<\/h3>\n
\nOccasionally \u2014and not just on Black Friday\u2014 the company has had such problems, so it deploys Pandora FMS as the computer of its own Enterprise, collecting real-time data.
\nPandora FMS learns through deep learning and considers the thresholds defined in the Metaconsole or Command Center (not naming it \u201cBridge\u201d is a missed Trekkie opportunity).
\nSoon, it sends an alert predicting that, for instance, in 30\u201360 minutes, the database cluster will reach 95%, degrading the catalog service and causing a painfully slow user experience.
\nAs we see, it not only prevents or predicts failures but also optimizes<\/strong>, enabling the organization to improve service satisfaction and meet potential contractual SLAs (Service Level Agreements).<\/p>\nWhat are the benefits and challenges of deep learning in IT?<\/h2>\n
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How do Pandora FMS and Pandora ITSM integrate intelligence based on deep learning?<\/h2>\n
What is the future of deep learning in IT management?<\/h3>\n
What is the future of generative AI in IT?<\/h2>\n
FAQs<\/h2>\n
What is deep learning and how is it applied in IT?<\/h4>\n
\nPandora FMS and ITSM use it to handle the heavy lifting of monitoring, management, and support.<\/p>\nWhat advantages does deep learning offer over traditional machine learning?<\/h4>\n
How does deep learning improve anomaly detection in IT?<\/h4>\n
\nThis reduces noise, anticipates problems, and supports effective resolution.<\/p>\nHow is deep learning applied in ITSM and support automation?<\/h4>\n