IT operations deal with a huge volume of data every day which puts tremendous pressure on the IT workforce. In addition, this also results in loss of optimization and real-time monitoring and resolution of issues. Artificial Intelligence in IT Operations or AIOps, efficiently handles these basic tasks to reduce the burden on IT operations and automates basic functions like monitoring, service desk, technical support amongst other solutions.
What is AIOps and how it can help your company?
AIOps works on three aspects: monitoring, engaging, and acting on big data. AIOps basically includes the application of machine learning and big data in IT operations. AIOps not just benefits IT and cloud-based companies but also see implementation in healthcare, finance, insurance, and other sectors.
Some use cases of AIOps include:
- Automation tools for service desk
- Realtime user monitoring tools
- Application performance monitoring
- Ingestion of data to recognize events and remediation
AIOps monitors data across IT systems, devices, and processes and helps companies control the ace at the following:
- Root-cause Analysis
- Anomaly Detection
- Realtime Notification
- Automated Event Management
- Dependency Mapping
This results in reduced costs for companies and less reliance on the human workforce. It also helps in scaling down errors and increasing the productivity of the workforce by organizing shifts for a smooth experience. It offers service reliability as the tool can be operated 24/7.
For example, AIOps in healthcare can easily replace the helpdesk and takeover booking appointments, generating triggers for issues, flagging important and emergency requests along with assigning them to the relevant teams. The prediction and remediation of issues can be a game-changer in the healthcare industry.
How to choose the right AIOps tool for your business
While the application of AIOps is very beneficial for a company, the implementation of the right tool is critical. So how do you identify the best AIOps solution for your business? All AIOps tools do not fit every business. Choosing the right tool depends on a match between your company’s IT goals and the features offered by the AIOps tool. The suitability of the two will determine whether an AIOps product works for your company.
Here are some factors to consider while picking an AIOps tool
- Complexity – The first factor is the level of complexity involved in your business. Higher complex environments require expensive AIOps to be deployed with better features. Understand what kind of features and functions are helpful for your business before implementing an AIOPs tool for your business. AIOps do not reduce complexity but give the company a tool to deal with large sets of data and process it in real-time for better decision making.
- Monitoring – The monitoring features of an AIOps tool are critical while selecting the right tool. However, it is not limited to only monitoring. A tool cannot entirely be considered AIOps if it offers only storage and retrieval of data.
- Connectivity – Connectivity to systems varies for every company and finding an AIOPs tool that offers connectivity to systems like Kubernetes, SAP and others is important. It isn’t easy to deploy such connectivity on your own. It is easy to determine what kind of connectivity your business needs. The factors involved include connectivity to a system and the ability to gather data while controlling that system.
- Return on investment – To measure returns on AIOps, you need historical data and monitor the progress. Typically, the ROI can be measured within 6 months of deployment. The result may not yield 100% results, but it definitely offers increased efficiency. One must also take into consideration the time taken to resolve issues using the human workforce to measure the value of your investment.
- Observability – Through observability, companies can monitor internal systems and use predictive analytics models to find anomalies and detect issues. After detection, the companies can then administer resolution and remediation of such issues. It also helps companies in being proactive in finding solutions for issues and predicting and detecting abnormal behaviors.
- Root-cause analysis – To know the origin of a failure or issue is one of the main features that help businesses trace and remedy an incident or event. Root-cause analysis helps businesses understand the primary cause even in complex and interdependent systems. AIOps tools that provide this feature, help companies that have multi-dependent and interwoven systems.
- Automation features – It is not enough for many businesses to ingest, correlate and understand data, events, and anomalies. The deployment of automated remedies not just saves time and effort but also reduces the costs involved. Automation features replace manual labor and save on human resource costs. It also helps in 24/7 monitoring and resolution which is beneficial to both the company and customers.
Choosing the right AIOps tools varies from company to company as their IT operating systems and requirements are different. However, understanding what your IT infrastructure needs, charting your AIOps transformation journey, and aligning it with your business goals can help you pick the right tool for your business.
Would you like an automatic computer update in the middle of booking the only available plane ticket? Imagine that in the context of an organization. While updating or maintaining a software system, the whole IT infrastructure should not come to a standstill. Organizations must ensure service availability while updating or maintaining their software systems. For adding a new microservice, organizations cannot shut down the entire IT system as it would affect service reliability. This is where containers and Kubernetes come into action.
Due to the recent COVID pandemic, the demand for virtual desktop infrastructure solutions has increased. While virtual machines are helpful in the current remote working culture, there are issues with deploying multiple applications. If multiple applications are deployed on a VDI desktop virtualization software, changes to shared dependencies can cause system failures. In order to not compromise with their service availability, firms decided to deploy only one application per virtual machine. However, as evident, a firm using multiple applications cannot use too many virtual machines due to cost constraints.
Containers were introduced to solve the problem of conflicting dependencies while deploying applications on virtual machines. Each container has its own storage, processing power, CPU, and file systems. Since a container has its own operating system, it can be easily decoupled from other applications on a virtual machine. You do not have to affect your service availability each time for adding a new application to your VDI desktop virtualization software. It can run anything from a small microservice to a large application.
Kubernetes (K8s) is an open-source platform for managing the deployment of applications in containers. Launched by Google, K8s can help you run applications on virtual machines without affecting the service availability. The process of managing groups of containers is known as orchestration in the IT world. The functionalities of Kubernetes are as follows:
- K8s decide the appropriate place to deploy your containers by analyzing their resource needs.
- K8s always have a backup container if any container crashes during deployment.
- K8s can manipulate the number of instances based on the CPU requirements.
- The non-volatile storage used by applications inside containers can be managed by K8s.
- K8s are responsible for load balancing of IP address and DNS.
- During an update, K8s closely monitor the health of the instances that are being introduced. If the update crashes, K8s help in restoring the previous version immediately without hampering the service availability.
Why Use Kubernetes?
Kubernetes has many positives:
- Kubernetes is highly portable allowing IT teams to deploy newer applications easily. Firms do not have to change the architecture of their IT infrastructure for adding a new application to virtual machines.
- Besides virtual machines, you can use K8s for deploying containers on cloud environments. With several use cases, IT teams can scale much faster without hampering service reliability.
- K8s is open-source and comes with its cost benefits.
- K8s offer enhanced availability enabling organizations to improve their service availability.
Breaking down the Architecture of K8s
Kubernetes follows the master-slave architecture as it has one master and multiple worker nodes. The master and worker nodes of K8s are explained below:
- Kubernetes Master – For a collection of servers, Kubernetes Master is the central controlling unit. Across each cluster, the networking and communication aspect is managed by the Kubernetes Master. It uses an API server that manages requests from various worker nodes. It also consists of a controller manager to maintain the shared state of a group of servers. It is the main reason why K8s ensure service availability at all times. Other components of Kubernetes Master are Etcd storage and Kubernetes scheduler.
- Worker Nodes – Kubernetes Master decides the workload of various worker nodes. The worker nodes consist of Kubelet that is responsible for monitoring the health of containers. If a worker node fails during deployment, another healthy pod is launched immediately to maintain the service availability. A pod is the structural unit of K8s that represents the workloads that are to be deployed.
Why AIOps is being used with Containers?
AIOps (Artificial Intelligence for IT Operations) is known for its application performance monitoring capabilities. However, organizations are using Kubernetes with an AIOps based analytics platform to achieve better results. An AIOps based analytics platform will offer high observability inside containers. IT teams can correlate the data generated by Kubernetes and system alerts to find the root cause of a particular IT incident. Besides managing current issues with the deployment of containers, an AIOps based analytics platform will also help you in identifying future issues.
In a nutshell
The global Kubernetes solutions market has grown in recent years. The AIOps global market worth is also growing and will be around USD 20 billion by the end of 2025. Start using Kubernetes and AIOps to boost your service availability!
Financial institutions use business applications to provide services to their users. They have to continuously monitor the performance of business applications to enhance service reliability. During peak business time, the number of impactful incident increase that downgrades the performance of business applications. IT experts have to spend more time addressing the incidents one by one. Financial institutions can use an AI-based platform for maintaining business continuity and service reliability. Let us know how ZIF enhances services reliability for financial institutions via AIOps.
What is service reliability?
Financial institutions are undergoing digital transformation quickly. For providing a digital user experience, financial institutions use software systems, applications, etc. The business applications need to perform continuously according to their specifications. If the performance gets deteriorated, business applications may experience downtime. It will have a direct effect on the ROI (Return on Investment).
Service reliability ensures that all the business applications or software systems are error-free. It ensures the continuous performance of IT systems within any financial institution. Business applications should live up to their expectations without any technical error. Financial institutions that have better service reliability also have larger uptime. Service reliability is usually expressed in percentage by IT experts.
What is AIOps?
AIOps (Artificial Intelligence for IT Operations) is used for automating and enhancing IT processes. AIOps uses the mixture of AI and ML algorithms to induce automation in IT processes. In this competitive era, AIOps can help a business optimize its IT infrastructure. IT strategies can be deployed at a large scale using AIOps.
The use of AI in IT operations can reduce the toll on IT experts as they don’t have to work overtime. Any issues with the IT infrastructure can be addresses in real-time using AI. AIOps platforms have gained popularity in recent times due to the challenges posed by the COVID pandemic. Financial institutions can also use an AIOps platform for better DEM (Digital Experience Monitoring).
What is ZIF?
ZIF (Zero Incident Framework) is an AIOps platform launched by GAVS Technologies. The goal of ZIF is to lead organizations towards a zero-incidence scenario. Any incidents within the IT infrastructure can be solved in real-time via ZIF. ZIF is more than just an ordinary TechOps platform. It can help financial institutions to monitor the performance of business applications as well as automate incidence reporting.
Service reliability engineers have to spend hours solving an incidence within the IT infrastructure. The downtime experienced can cost a financial institution more than expected. ZIF is an AI-based platform and will help you in automating responses to incidents within the IT infrastructure. ZIF can help financial institutions gain an edge over their competitors and ensuring business continuity.
Why use ZIF for your financial institution?
ZIF has multiple use cases for a financial institution. If you are facing any of these below-mentioned challenges, you can use ZIF to solve them:
- A financial institution may receive alerts at frequent intervals from the current IT monitoring system. An institution may not have enough workforce or time to address such a high volume of alerts.
- Useful IT operations for a financial institution may face unexpected downtime. It not only impacts the ROI but also drives the customer away.
- High-impact incidents within the IT infrastructure may reduce the service reliability of a financial institution.
- A financial institution may have poor observability of the user experience. It will lead to the inability in providing a personalized digital experience to customers.
- The IT staff of a financial institution may burn out due to the excessive number of incidents being reported. Manual efforts will stop after a certain number of incidents.
How ZIF is the solution?
The functionalities of ZIF that can solve the above-mentioned challenges are as follows:
- ZIF can monitor all components of the IT infrastructure like storage, software system, server, and others. ZIF will perform full-stack monitoring of the IT infrastructure with less human effort.
- ZIF performs APM (Application Performance Monitoring) to measure the performance and accuracy of business applications.
- It can perform real-time APM for improving the user experience.
- It can take data from business applications and can identify relationships between the data. Event correlation alerts by ZIF will also inform you during system outages or failures.
- ZIF can make intelligent predictions for identifying future incidents.
- ZIF can help a financial institution in mitigating an IT issue before it leaves its impact on operations.
What are the outcomes and benefits of ZIF?
The outcomes of using ZIF for your financial institution are as follows:
- Efficiency: With ZIF, you can enhance the efficiency of your IT tools and technologies. When your IT framework is more efficient, you can experience better service reliability.
- Accuracy: ZIF will provide you with predictive insights that can increase the accuracy of business applications. IT operations can be led proactively with the aid of ZIF.
- Reduction in incidents: ZIF will help you in identifying frequent incidents and solving them once and for all. The number of incidents per user can be decreased by the use of ZIF.
- MTTD: ZIF can help you identifying incidents in real-time. Reduced MTTD (Mean Time to Detect) will have a direct impact on the service reliability.
- MTTR: ZIF will reduce the MTTR (Mean Time to Resolve) for your financial institution. With reduced MTTR, you can offer better service reliability.
- Cost optimization: ZIF can replace costly IT operations with cost-effective solutions. If any IT operation is not adding any value to your institution, it can be identified with the aid of ZIF.
ZIF can help you in automating various IT processes like monitoring, incident reporting, and others. Your employees can focus on providing diverse financial services to customers besides worrying about the user interface. ZIF is a cost-effective AIOps solution for your financial institution.
In a nutshell
The CAGR (Compound Annual Growth Rate) of the global AIOps industry is more than 25%. Financial institutions are also using AI for intelligent IT operations and better service reliability. Service reliability engineers in your organization will have to put fewer manual efforts with the help of ZIF. Use ZIF for enhancing service reliability!
A pertinent question for the post COVID workforce is, can empathy be learnt? Should it be practiced only by the leaders, or by everyone – can it be seamlessly woven into the fabric of the organization? We are seeing that dynamics at play for remote teams is little unpredictable, making each day uniquely challenging. Empathy is manifested through mindful behaviours, where one’s action is recognized as genuine, personal, and specific to the situation. A few people can be empathetic all the time, a few, practice it consciously, and a few are unaware of it.
Empathy is a natural human response that can be practiced by everyone at work for nurturing an environment of trust. We often confuse empathy for sympathy – while sympathy is feeling sorry for one’s situation, empathy is understanding one’s feelings and needs, and putting the effort to offer authentic support. It requires a shift in perspective, and building trust, respect, and compassion at a deeper level. As Satya Nadella, CEO, Microsoft says, “Empathy is a muscle that needs to be exercised.”
Here are three ways to consciously practice empathy at work –
- Going beyond yourself
It takes a lot to forget how we feel that day, or what is priority for us. However, to be empathetic, one needs to be less judgemental. When one is consciously practicing empathy, one needs to be patient with yourself, your thoughts, and not compare yourself with the person you are empathizing with. If we get absorbed by our own needs, it gets difficult to be generous and compassionate. We need to remember empathy leads to influence and respect, and for that we should not get blind sighted by our perceptions.
- Being a mindful and intentional listener
While practicing empathy, one has refrain from criticism, and be mindful of not talking about one’s problems. We may get sympathetic and give unsolicited advice. Sometimes it only takes to be an intentional listener, by avoiding distractions, and having a very positive body language, and demeanour. This will enable us to ask right questions and collaborate towards a solution.
- Investing in the person
Very often, we support our colleagues and co-workers by responding to their email requests. However, by building positive workplace relationships, and knowing the person beyond his/her email id, makes it much easier to foster empathy. Compassion needs to be not just in words, but in action too, and that can happen only by knowing the person. Taking interest in a co-worker or a team member, beyond a professional capability, does not come out of thin air. It takes conscious continuous efforts to get to know the person, showing care and concern, which will help us to relate to the myriad challenges they go through – be it chronic illness, child care that correlates to his/her ability to engaged at work. It will enable us to personalize the experience, and see the person’s point of view, holistically.
When we take that genuine interest in how we make others feel and experience, we start mindfully practicing empathy. Empathy fosters respect. Empathy helps resolves conflicts better, empathy builds stronger teams, empathy inspires one another to work towards collective goals, and empathy breaks authority. Does it take that extra bit of time to consciously practice it? Yes, but it is all worth it.
The explosion of technology & data is impacting every aspect of business. While modern technologies have enabled transformational digitalization of enterprises, they have also infused tremendous complexities in infrastructure & applications. We have reached a point where effective management of IT assets mandates supplementing human capabilities with Artificial Intelligence & Machine Learning (AI/ML).
AIOps is the application of Artificial Intelligence (AI) to IT operations (Ops). AIOps leverages AI/ML technologies to optimize, automate, and supercharge all aspects of IT Operations. Gartner predicts that the use of AIOps and digital experience monitoring tools for monitoring applications and infrastructure would increase by 30% in 2023. In this blog, we hope to debunk some common misconceptions about AIOps.
MYTH 1: AIOps mainly involves alert correlation and event management
AIOps can deliver enormous value to enterprises that harness the wide range of use cases it comes with. While alert correlation & management are key, AIOps can add a lot of value in areas like monitoring, user experience enhancement, and automation.
AIOps monitoring cuts across infrastructure layers & silos in real-time, focusing on metrics that impact business outcomes and user experience. It sifts through monitoring data clutter to intelligently eliminate noise, uncover patterns, and detect anomalies. Monitoring the right UX metrics eliminates blind spots and provides actionable insights to improve user experience. AIOps can go beyond traditional monitoring to complete observability, by observing patterns in the IT environment, and externalizing the internal state of systems/services/applications. AIOps can also automate remediation of issues through automated workflows & standard operating procedures.
MYTH 2: AIOps increases human effort
Forbes says data scientists spend around 80% of their time preparing and managing data for analysis. This leaves them with little time for productive work! With data pouring in from monitoring tools, quite often ITOps teams find themselves facing alert fatigue and even missing critical alerts.
AIOps can effectively process the deluge of monitoring data by AI-led multi-layered correlation across silos to nullify noise and eliminate duplicates & false positives. The heavy lifting and exhausting work of ingesting, analyzing, weeding out noise, correlating meaningful alerts, finding the probable root causes, and fixing them, can all be accomplished by AIOps. In short, AIOps augments human capabilities and frees up their bandwidth for more strategic work.
MYTH 3: It is hard to ‘sell’ AIOps to businesses
While most enterprises acknowledge the immense potential for AI in ITOps, there are some concerns that are holding back widespread adoption. The trust factor with AI systems, the lack of understanding of the inner workings of AI/ML algorithms, prohibitive costs, and complexities of implementation are some contributing factors. While AIOps can cater to the full spectrum of ITOps needs, enterprises can start small & focus on one aspect at a time like say alert correlation or application performance monitoring, and then move forward one step at a time to leverage the power of AI for more use cases. Finding the right balance between adoption and disruption can lead to a successful transition.
MYTH 4: AIOps doesn’t work in complex environments!
With Machine Learning and Big Data technologies at its core, AIOps is built to thrive in complex environments. The USP of AIOps is its ability to effortlessly sift through & garner insights from huge volumes of data, and perform complex, repetitive tasks without fatigue. AIOps systems constantly learn & adapt from analysis of data & patterns in complex environments. Through this self-learning, they can discover the components of the IT ecosystem, and the complex network of underlying physical & logical relationships between them – laying the foundation for effective ITOps.
MYTH 5: AIOps is only useful for implementing changes across IT teams
An AIOps implementation has an impact across all business processes, and not just on IT infrastructure or software delivery. Isolated processes can be transformed into synchronized organizational procedures. The ability to work with colossal amounts of data; perform highly repetitive tasks to perfection; collate past & current data to provide rich inferences; learn from patterns to predict future events; prescribe remedies based on learnings; automate & self-heal; are all intrinsic features that can be leveraged across the organization. When businesses acknowledge these capabilities of AIOps and intelligently identify the right target areas within their organizations, it will give a tremendous boost to quality of business offerings, while drastically reducing costs.
MYTH 6: AIOps platforms offer only warnings and no insights
With its ability to analyze and contextualize large volumes of data, AIOps can help in extracting relevant insights and making data-driven decisions. With continuous analysis of data, events & patterns in the IT environment – both current & historic – AIOps acquires in-depth knowledge about the functioning of the various components of the IT ecosystem. Leveraging this information, it detects anomalies, predicts potential issues, forecasts spikes and lulls in resource utilization, and even prescribes appropriate remedies. All of this insight gives the IT team lead time to fix issues before they strike and enables resource optimization. Also, these insights gain increasing precision with time, as AI models mature with training on more & more data.
MYTH 7: AIOps is suitable only for Operations
AIOps is a new generation of shared services that has a considerable impact on all aspects of application development and support. With AIOps integrated into the dev pipeline, development teams can code, test, release, and monitor software more efficiently. With continuous monitoring of the development process, problems can be identified early, issues fixed, and changes rolled back as appropriate. AIOps can promote better collaboration between development & ops teams, and proactive identification & resolution of defects through AI-led predictive & prescriptive insights. This way AIOps enables a shift left in the development process, smarter resource management, and significantly improves software quality & time to market.
In 2017, Gartner coined the term ‘augmented analytics’ and claimed it would be the future of data analytics. They predicted it would be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics.
Here is the why and how.
Most organizations depend on data to back up its decision-making and strategy. Organizations collect data on all accounts of processes and events; thus, analyzing and effectively managing the breadth of this data is challenging yet significant for mining it for business insights.
Traditional business intelligence tools have given way to a new generation of business intelligence tools – Augmented Analytics technology.
Augmented Analytics is an approach of data analytics that employs machine learning (ML) and natural language processing (NLP) to automate and improve data access and data quality, uncover hidden patterns and correlations in data, pinpoint what’s driving results, predict future results and suggest actions to maximize or minimize desirable or undesirable outcomes.
Augmented Analytics is designed to conduct analyses and generate business insights automatically with little to no supervision and can be used without needing the assistance of a business analyst or data scientist. However, the focus of Augmented Analytics stays in its assistive role, where technology does not replace humans but supports them.
Evolution of Analytics
Business Intelligence (BI) and Analytics has evolved, increasing the demand for decision making through data analytics. It drives to unfold from traditional mirror reporting into self-service Business Intelligence and analytics.
Despite the advances in self-service analytics with agile discovery, many businesses demand assistance to uncover insights in data.
The next generation of BI and analytics products are augmented with artificial intelligence (AI) including ML, which automates complex analytics processes, and NLP makes it easier for users without knowledge of data science or query languages to obtain insights.
Augmented analytics offer starting-point suggestions and guidance to the users. It also empowers businesses to leverage more of their data to make better decisions when compared to the traditional and self-service Business Intelligence.
SAP Analytics Cloud
SAP Analytics Cloud (SAC) is an analytical solution that features all the analytics functionalities like business intelligence, augmented analytics, predictive analytics, enterprise planning, and application building in one intuitive user interface. It is empowered with ML and built-in AI that helps discover in-depth insights, simplify access to critical information and enable adequate decision-making.
Augmented SAP Analytics Cloud
Augmented analytics capabilities offered by SAP Analytics Cloud empowers business intelligence to reap the benefits of AI and ML.
SAP Analytics Cloud facilitates users to interact with the system using natural language to gather automatic insights, where Predictive Scenarios offer an accessible way into Predictive Analytics using the past data to foresee the future.
Let’s look at the Analytics features, and capabilities offered by SAP Analytics Cloud
Search to Insight – Query search in Natural Language
The Search to Insight feature enables query search through natural language through conversational AI and NLP. No knowledge of query languages like SQL, R, or Python is required. Asking questions just like in a search engine or digital personal assistant fetches insightful answers represented by visualization or numeric values tailored based on the question type.
Search to Insight provides auto-complete suggestions to match words or phrases in questions for measures and dimensions in the data and includes auto spell-check.
Smart Insights – Instant explanations
The Smart Insights feature facilitates digging deeper into the data points. It analyzes the underlying dataset and runs various statistical algorithms to offer insights based on the current user context.
It helps to understand top contributors of specific data points without having to manually pivot or slice and dice the data. When a data point is selected, ML calculations run on information that is of the same nature as the selected data point. For example, if the selected data point is ‘Total Revenue’, the top contributors are based on ‘Total Revenue’. It analyzes the dimension in the selected data and looks for members in these dimensions that influence the selected value.
Smart Discovery – Easily reveal insights
The Smart Discovery feature identifies hidden patterns and statically relevant relationships in the data to discover how business factors influence performance. It helps to understand the business drivers behind the core KPIs.
Based on the selection of measure or dimension, smart discovery automatically generates interactive story pages as below –
Overview: It explains the data distribution, summary of trends, and the detected patterns for the target dimension or measure.
Key Influencers: It explains the influence of the dimensions for the value of the target measures in the context of the selected model using classification and regression techniques, where the classification techniques are used to identify dimensions that segregate results into different groups of results and the regression techniques identify relationships between data points to predict future results.
Unexpected Values: It displays the details about outliers, where the actual values differ greatly from what the predictive model would expect. If an actual value diverges from the regression line it is categorized as unexpected.
Simulation: The simulation facilitates the ‘what-if’ analysis, users can change the values of the measures and dimensions to see the predicted change positively, negatively, or neutrally in the target measure.
Smart Predict – Answers the toughest questions
Smart Predict feature predicts the likelihood of different outcomes based on the historical data using techniques such as data mining, statistics, machine learning, and artificial intelligence.
Smart Predict, also referred as Predictive forecasting, considers different values, trends, cycles, and/or fluctuations in the data to make predictions that can be leveraged to aid business planning processes.
Smart Predict provides 3 different predictive scenario options for selection
Classification: It can be used to generate predictions for a binary event. For example, whether individual customers would be likely to buy the target product or not.
Time Series: It can be used to forecast values over a set period. For example, forecasting the sales of product by month or week, using historical data.
Regression: It can be used to predict values and explore key values behind them. For example, predicting the price of an imported product based on projected duties or shipping charges.
In the modern world of business Intelligence, SAP Analytics cloud’s ML technology augments the analytic process which assists from insights to actions and enables avoiding the agenda-driven and biased decision making by revealing the accurate patterns which drives the business.
Business Environment Overview
In this pandemic economy, the topmost priorities for most companies are to make sure the operations costs and business processes are optimized and streamlined. Organizations must be more proactive than ever and identify gaps that need to be acted upon at the earliest.
The industry has been striving towards efficiency and effectivity in its operations day in and day out. As a reliability check to ensure operational standards, many organizations consider the following levers:
- High Application Availability & Reliability
- Optimized Performance Tuning & Monitoring
- Operational gains & Cost Optimization
- Generation of Actionable Insights for Efficiency
- Workforce Productivity Improvement
Organizations that have prioritized the above levers in their daily operations require dedicated teams to analyze different silos and implement solutions that provide the result. Running projects of this complexity affects the scalability and monitoring of these systems. This is where AIOps platforms come in to provide customized solutions for the growing needs of all organizations, regardless of the size.
Deep Dive into AIOps
Artificial Intelligence for IT Operations (AIOps) is a platform that provides multilayers of functionalities that leverage machine learning and analytics. Gartner defines AIOps as a combination of big data and machine learning functionalities that empower IT functions, enabling scalability and robustness of its entire ecosystem.
These systems transform the existing landscape to analyze and correlate historical and real-time data to provide actionable intelligence in an automated fashion.
AIOps platforms are designed to handle large volumes of data. The tools offer various data collection methods, integration of multiple data sources, and generate visual analytical intelligence. These tools are centralized and flexible across directly and indirectly coupled IT operations for data insights.
The platform aims to bring an organization’s infrastructure monitoring, application performance monitoring, and IT systems management process under a single roof to enable big data analytics that give correlation and causality insights across all domains. These functionalities open different avenues for system engineers to proactively determine how to optimize application performance, quickly find the potential root causes, and design preventive steps to avoid issues from ever happening.
AIOps has transformed the culture of IT war rooms from reactive to proactive firefighting.
Industrial Inclination to Transformation
The pandemic economy has challenged the traditional way companies choose their transformational strategies. Machine learning powered automation for creating an autonomous IT environment is no longer a luxury. he usage of mathematical and logical algorithms to derive solutions and forecasts for issues have a direct correlation with the overall customer experience. In this pandemic economy, customer attrition has a serious impact on the annual recurring revenue. Hence, organizations must reposition their strategies to be more customer centric in everything they do. Thus, providing customers with the best-in-class service coupled with continuous availability and enhanced reliability has become an industry-standard.
As reliability and scalability are crucial factors for any company’s growth, cloud technologies have seen a growing demand. This shift of demand for cloud premises for core businesses has made AIOps platforms more accessible and easier to integrate. With the handshake between analytics and automation, AIOps has become a transformative technology investment that any organization can make.
As organizations scale in size, so does the workforce and the complexity of the processes. The increase in size often burdens organizations with time-pressed teams having high pressure on delivery and reactive housekeeping strategies. An organization must be ready to meet the present and future demands with systems and processes that scale seamlessly. This why AIOps platforms serve as a multilayered functional solution that integrates the existing systems to manage and automate tasks with efficiency and effectivity. When scaling results in process complexity, AIOps platforms convert the complexity to effort savings and productivity enhancements.
Across the industry, many organizations have implemented AIOps platforms as transformative solutions to help them embrace their present and future demand. Various studies have been conducted by different research groups that have quantified the effort savings and productivity improvements.
The AIOps Organizational Vision
As the digital transformation race has been in full throttle during the pandemic, AIOps platforms have also evolved. The industry did venture upon traditional event correlation and operations analytical tools that helped organizations reduce incidents and the overall MTTR. AIOps has been relatively new in the market as Gartner had coined the phrase in 2016. Today, AIOps has attracted a lot of attention from multiple industries to analyze its feasibility of implementation and the return of investment from the overall transformation. Google trends show a significant increase in user search results for AIOps during the last couple of years.
While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:
- Understanding the feasibility and concerns for its future adoption
- Classification of business processes and use cases for AIOps intervention
- Quantification of operational gains from incident management using the functional AIOps tools
AIOps is truly visioned to provide tools that transform system engineers to reliability engineers to bring a system that trends towards zero incidents.
Because above all, Zero is the New Normal.
ZIF™ is the only AIOps platform that is powered with true machine learning algorithms with the capability to self-learn and adapt to today’s modern IT infrastructure.
ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Return on investment can be measured based on the outcomes the platform has delivered. Users get to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.
Single Pane of Action – Unified View across IT Enterprise
The biggest challenge IT Operations teams have been trying to tackle over the years is to get a bird’s eye view on what is happening across their IT landscape. The more complex the enterprise becomes the harder it becomes for the IT Operations team to understand what is happening across their enterprise. ZIF solves this issue with ease.
The capability to ingest data from any source monitoring or ITSM tool has helped IT organizations to have a real-time view of what is happening across their landscape. Enormous time can be saved by the IT engineers with ZIF’s unified view, who would otherwise be traversing between multiple monitoring tools.
ZIF can integrate with 100+ tools to ingest (static/dynamic) data in real-time via ZIF Universal Connector. This is a low code component of ZIF and dataflows within the connector can also be templatized for reuse.
Intelligence – Reduction in MTTR – Correlation of Alerts/Events
Approximately 80% of the time is lost by IT engineers in identifying the problem statement for an incident. This has been costing billions of dollars for enterprises. ZIF, with the help of Artificial Intelligence, can reduce the mean time to identify the probable root cause of the incident within seconds. The high-performance correlation engine that runs under the hood of the platform process millions of patterns that the platform has learned from the historical data and correlates the sequences that are happening in real-time and creates cases. These cases are then assigned to IT engineers with the probable root cause for them to fix the issue. This increases the productivity of the IT engineers resulting in better revenue for organizations.
Intelligence – Predictive Analytics
AIOps platforms are incomplete without the Predictive Analytics capability. ZIF has adopted unsupervised machine learning algorithms to perform predictive analytics on the utilization data that is ingested into the platform. These algorithms can learn trends and understand the symptoms of an incident by analyzing tons of data that the platform had consumed over a period. Based on the analysis, the platform generates opportunity cards that help IT engineers take proactive measures on the forecasted incident. These opportunity cards are generated a minimum of 60 minutes in advance which gives the engineers a lead time to fix an issue before it strikes the landscape.
Visibility – Auto-Discovery of IT Assets & Applications
ZIF agentless discovery is a seamless discovery component, that helps in identifying all the IP assets that are available in an enterprise. Just not discovering the assets, but the component also plots a physical topology & logical map for better consumption of the IT engineers. This gives a very detailed view of every asset in the IT landscape. The logical topology gives in-depth insights into the workload metrics that can be utilized for deep analytics.
Visibility – Cloud Monitoring
In today’s digital transformation journey, cloud is inevitable. To have a better control over the cloud orchestrated application, enterprises must depend on the monitoring tools provided by cloud providers. The lack of insights often leads to the unavailability of applications for end-users. More than monitoring, insights that help enterprises take better-informed decisions are the need of the hour.
ZIF’s cloud monitoring components can monitor any cloud instance. Data that are generated from the provider provided monitoring tools are ingested into ZIF to further analyze the data. ZIF can connect to Azure, AWS & Google Cloud to derive data-driven insights.
Optimization – Remediation – Autonomous IT Operations
ZIF does not stop by just providing insights. The platform deploys the right automation bot to remediate the incident.
ZIF has 250+ automation bots that can be deployed to fast-track the resolution process by a minimum of 90%. Faster resolutions result in increased uptime of applications and better revenue for the enterprise.
Sample ZIF bots:
- Service Restart / VM Restart
- Disk Space Clean-up
- IIS Monitoring App Pool
- Dynamic Resource Allocation
- Process Monitoring & Remediation
- DL & Security Group Management
- Windows Event Log Monitoring
- Automated phishing control based on threat score
- Service request automation like password reset, DL mapping, etc.
For more information on ZIF, please visit www.zif.ai
In my previous article (zif.ai/inverse-reinforcement-learning/), I had introduced Inverse Reinforcement Learning and explained how it differs from Reinforcement Learning. In this article, let’s explore Generative Adversarial Networks or GAN; both GAN and reinforcement learning help us understand how deep learning is trying to imitate human thinking.
With access to greater hardware power, Neural Networks have made great progress. We use them to recognize images and voice at levels comparable to humans sometimes with even better accuracy. Even with all of that we are very far from automating human tasks with machines because a tremendous amount of information is out there and to a large extent easily accessible in the digital world of bits. The tricky part is to develop models and algorithms that can analyze and understand this humongous amount of data.
GAN in a way comes close to achieving the above goal with what we call automation, we will see the use cases of GAN later in this article.
This technique is very new to the Machine Learning (ML) world. GAN is a deep learning, unsupervised machine learning technique proposed by Ian Goodfellow and few other researchers including Yoshua Bengio in 2014. One of the most prominent researcher in the deep learning area, Yann LeCun described it as “the most interesting idea in the last 10 years in Machine Learning”.
What is Generative Adversarial Network (GAN)?
A GAN is a machine learning model in which two neural networks compete to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.
The logic of GANs lie in the rivalry between the two Neural Nets. It mimics the idea of rivalry between a picture forger and an art detective who repeatedly try to outwit one another. Both networks are trained on the same data set.
A generative adversarial network (GAN) has two parts:
- The generator (the artist) learns to generate plausible data. The generated instances become negative training examples for the discriminator.
- The discriminator (the critic) learns to distinguish the generator’s fake data from real data. The discriminator penalizes the generator for producing implausible results.
GAN can be compared with Reinforcement Learning, where the generator is receiving a reward signal from the discriminator letting it know whether the generated data is accurate or not.
During training, the generator tries to become better at generating real looking images, while the discriminator trains to be better classify those images as fake. The process reaches equilibrium at a point when the discriminator can no longer distinguish real images from fakes.
Here are the steps a GAN takes:
- The input to the generator is random numbers which returns an image.
- The output image of the generator is fed as input to the discriminator along with a stream of images taken from the actual dataset.
- Both real and fake images are given to the discriminator which returns probabilities, a number between 0 and 1, 1 meaning a prediction of authenticity and 0 meaning fake.
So, you have a double feedback loop in the architecture of GAN:
- We have a feedback loop with the discriminator having ground truth of the images from actual training dataset
- The generator is, in turn, in a feedback loop along with the discriminator.
Most GANs today are at least loosely based on the DCGAN architecture (Radford et al., 2015). DCGAN stands for “deep, convolution GAN.” Though GANs were both deep and convolutional prior to DCGANs, the name DCGAN is useful to refer to this specific style of architecture.
Applications of GAN
Now that we know what GAN is and how it works, it is time to dive into the interesting applications of GANs that are commonly used in the industry right now.
Can you guess what’s common among all the faces in this image?
None of these people are real! These faces were generated by GANs, exciting and at the same time scary, right? We will focus about the ethical application of the GAN in the article.
GANs for Image Editing
Using GANs, appearances can be drastically changed by reconstructing the images.
GANs for Security
GANs has been able to address the concern of ‘adversarial attacks’.
These adversarial attacks use a variety of techniques to fool deep learning architectures. Existing deep learning models are made more robust to these techniques by GANs by creating more such fake examples and training the model to identify them.
Generating Data with GANs
The availability of data in certain domains is a necessity, especially in domains where training data is needed to model learning algorithms. The healthcare industry comes to mind here. GANs shine again as they can be used to generate synthetic data for supervision.
GANs for 3D Object Generation
GANs are quite popular in the gaming industry. Game designers work countless hours recreating 3D avatars and backgrounds to give them a realistic feel. And, it certainly takes a lot of effort to create 3D models by imagination. With the incredible power of GANs, wherein they can be used to automate the entire process!
GANs are one of the few successful techniques in unsupervised machine learning and it is evolving quickly and improving our ability to perform generative tasks. Since most of the successful applications of GANs have been in the domain of computer vision, generative model sure has a lot of potential, but is not without some drawbacks.