Analyze

Have you heard of AIOps?

Artificial intelligence for IT operations (AIOps) is an umbrella term for the application of Big Data Analytics, Machine Learning (ML) and other Artificial Intelligence (AI) technologies to automate the identification and resolution of common Information Technology (IT) problems. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor the assets and gain visibility into the working behaviour and dependencies between these assets.

According to a Gartner study, the adoption of AIOps by large enterprises would rise to 30% by 2023.

ZIF – The ideal AIOps platform of choice

Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™

ZIF comprises of 5 modules, as outlined below.

At the heart of ZIF, lies its Analyze and Predict (A&P) modules which are powered by Artificial Intelligence and Machine Learning techniques. From the business perspective, the primary goal of A&P would be 100% availability of applications and business processes.

Come, let us understand more about the Analyze function of ZIF.

With Analyzehaving a Big Data platform under its hood, volumes of raw monitoring data, both structured and unstructured, can be ingested and grouped to build linkages and identify failure patterns.

Data Ingestion and Correlation of Diverse Data

The module processes a wide range of data from varied data sources to break siloes while providing insights, exposing anomalies and highlighting risks across the IT landscape. It increases productivity and efficiency through actionable insights.

  • 100+ connectors for leading tools, environments and devices
  • Correlation and aggregation methods uncover patterns and relationships in the data

Noise Nullification

Eliminates duplicate incidents, false positives and any alerts that are insignificant. This also helps reduce the Mean-Time-To-Resolution and event-to-incident ratio.

  • Deep learning algorithms isolate events that have the potential to become incidents along with their potential criticality
  • Correlation and Aggregation methods group alerts and incidents that are related and needs a common remediation
  • Reinforcement learning techniques are applied to find and eliminate false positives and duplicates

Event Correlation

Data from various sources are ingested real-time into ZIF either by push or pull mechanism. As the data is ingested, labelling algorithms are run to label the data based on identifiers. The labelled data is passed through the correlation engine where unsupervised algorithms are run to mine the patterns. Sub-sequence mining algorithms help in identifying unique patterns from the data.

Unique patterns identified are clustered using clustering algorithms to form cases. Every case that is generated is marked by a unique case id. As part of the clustering process, seasonality aspects are checked from historical transactions to derive higher accuracy of correlation.

Correlation is done based on pattern recognition, helping to eliminate the need for relational CMDB from the enterprise. The accuracy of the correlation increases as patterns reoccur. Algorithms also can unlearn patterns based on the feedback that can be provided by actions taken on correlation. As these are unsupervised algorithms, the patterns are learnt with zero human intervention.

Accelerated Root Cause Analysis (RCA)

Analyze module helps in identifying the root causes of incidents even when they occur in different silos. Combination of correlation algorithms with unsupervised deep learning techniques aid in accurately nailing down the root causes of incidents/problems. Learnings from historical incidents are also applied to find root causes in real-time. The platform retraces the user journeys step-by-step to identify the exact point where an error occurs.

Customer Success Story – How ZIF’s A&P transformed IT Operations of a Manufacturing Giant

  • Seamless end-to-end monitoring – OS, DB, Applications, Networks
  • Helped achieve more than 50% noise reduction in 6 months
  • Reduced P1 incidents by ~30% through dynamic and deep monitoring
  • Achieved declining trend of MTTR and an increasing trend of Availability
  • Resulted in optimizingcommand centre/operations head count by ~50%
  • Resulted in ~80% reduction in operations TCO

For more detailed information on GAVS’ Analyze, or to request a demo please visit zif.ai/products/analyze

References: www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

ABOUT THE AUTHOR

Vasudevan Gopalan


Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.

Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.

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The Chatty Bots!

Chatbots can be loosely defined as software to simulate human conversation. They are widely used as textbots or voicebots in social media, in websites to provide the initial engagement with visitors, as part of  customer service/IT operations teams to provide tier 1 support round the clock and for various other organizational needs, as we’ll see later in the blog, in integration with enterprise tools/systems. Their prevalence can be attributed to how easy it has now become to get a basic chatbot up & running quickly, using the intuitive drag-drop interfaces of chatbot build tools. There are also many cloud-based free or low-cost AI platforms for building bots using the provided APIs. Most of these platforms also come with industry-specific content, add-on tools for analytics and more.

Rule-based chatbots can hold basic conversation with scripted ‘if/then’ responses for commonly raised issues/faqs, and redirect appropriately for queries beyond their scope. They use keyword matches to get relevant information from their datastore. Culturally, as we begin to accept and trust bots to solve problems and extend support; with companies beginning to see value in these digital resources; and with heavy investments in AI technologies, chatbots are gaining traction, and becoming more sophisticated. AI-led chatbots are way more complex than their rule-based counterparts and provide dynamically tailored, contextual responses based on the conversation and interaction history. Natural Language Processing capabilities give these chatbots the human-like skill to comprehend nuances of language and gauge the intent behind what is explicitly stated.    

The Artificial Neural Network(ANN) for Natural Language Processing(NLP) 

An ANN is an attempt at a tech equivalent of the human brain! You can find our blog on ANNs and Deep Learning here.

Traditional AI models are incapable of handling highly cognitive tasks like image recognition, image classification, natural language processing, speech recognition, text-speech conversion, tone analysis and the like. There has been a lot of success with Deep Learning approaches for such cerebral use cases. For NLP, handling the inherent complexities of language such as sentiment, ambiguity or insinuation, necessitates deeper networks and a lot of training with enormous amounts of data. Each computational layer of the network progressively extracts finer and more abstract details from the inputs, essentially adding value to the learnings from the previous layers. With each training iteration, the network adapts, auto-corrects and finetunes its weights using optimization algorithms, until it reaches a maturity level where it is almost always correct in spite of input vagaries. The USP of a deep network is that, armed with this knowledge gained from training, it is able to extract correlations & meaning from even unlabeled and unstructured data.

Different types of neural networks are particularly suited for different use cases. Recurrent Neural Networks(RNNs) are good for sequential data like text documents, audio and natural language. RNNs have a feedback mechanism where each neuron’s output is fed back as weighted input, along with other inputs. This gives them ‘memory’ implying they remember their earlier inputs, but with time the inputs get diluted by the presence of new data. A variant of the RNN helps solve this problem. Long Short Term Memory (LSTM) models have neurons(nodes) with gated cells that can regulate whether to ‘remember’ or ‘forget’ their previous inputs, thereby giving more control over what needs to be remembered for a long time versus what can be forgotten. For e.g.: it would help to ‘remember’ when parsing through a text document because the words and sentences are most likely related, but ‘forgetting’ would be better during the move from one text document to the next, since they are most likely unrelated.

The Chatbot Evolution

In the 2019 Gartner CIO Survey, CIOs identified chatbots as the main AI-based application used in their enterprises. “There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years”, says Van Baker, VP Analyst at Gartner.

Personal & Business communication morphs into the quickest, easiest and most convenient mode of the time. From handwritten letters to emails to phone calls to SMSs to mere status updates on social media is how we now choose to interact. Mr. Baker goes on to say that with the increase of millennials in the workplace, and their  demand for instant, digital connections, they will have a large impact on how quickly organizations adopt the technology.

Due to these evolutionary trends, more organizations than we think, have taken a leap of faith and added these bots to their workforce. It is actually quite interesting to see how chatbots are being put to innovative use, either stand-alone or integrated with other enterprise systems.

Chatbots in the Enterprise

Customer service & IT service management(ITSM) are use cases through which chatbots gained entry into the enterprise. Proactive personalized user engagement, consistency and ease of interaction, round-the-clock availability & timely address of issues have lent themselves to operational efficiency, cost effectiveness and enhanced user experience. Chatbots integrated into ITSM help streamline service, automate workflow management, reduce MTTR, and provide always-on services. They also make it easier to scale during peak usage times since they reduce the need for customers to speak with human staff, and the need to augment human resources to handle the extra load. ChatOps is the use of chatbots within a group collaboration tool where they run between the tool and the user’s applications and automate tasks like providing relevant data/reports, scheduling meetings, emailing, and ease the collaborative process between siloed teams and processes, like in a DevOps environment where they double up as the monitoring and diagnostic tool for the IT landscape.

In E-commerce, chatbots can boost sales by taking the customer through a linear shopping experience from item search through purchase. The bot can make purchase suggestions based on customer preferences gleaned from product search patterns and order history.

In Healthcare, they seamlessly connect healthcare providers, consumers and information and ease access to each other. These bot assistants come in different forms catering to specific needs like personal health coach, companion bot to provide the much-needed conversational support for patients with Alzheimer’s, confidant and therapist for those suffering from depression, symptom-checker to provide initial diagnosis based on symptoms and enable remote text or video consultation with a doctor as required and so on.

Analytics provide insights but often not fast enough for the CXO. Decision-making becomes quicker when executives can query a chatbot to get answers, rather than drilling through a dashboard. Imagine getting immediate responses to requests like Which region in the US has had the most sales during Thanksgiving? Send out a congratulatory note to the leadership in that region. Which region has had the poorest sales? Schedule a meeting with the team there. Email me other related reports of this region. As can be seen here, chatbots work in tandem with other enterprise tools like analytics tools, calendar and email to make such fascinating forays possible.

Chatbots can handle the mundane tasks of Employee Onboarding, such as verification of mandatory documents, getting required forms filled, directing them to online new-hire training and ensuring completion.

When integrated with IoT devices, they can help in Inventory Management by sending out notifications when it’s time to restock a product, tracking shipment of new orders and alerting on arrival.

Chatbots can offer Financial Advice by recommending investment options based on transactional history, current investments or amounts idling in savings accounts, alerting customer to market impact on current portfolio and so much more.

As is evident now, the possibilities of such domain-specific chatbots are endless, and what we have seen is just a sampling of their use cases!

Choosing the Right Solution

The chatbot vendor market is crowded, making it hard for buyers to fathom where to even begin. The first step is an in-depth evaluation of the company’s unique needs, constraints, main use cases and enterprise readiness. The next big step is to decide between off-the shelf or in-house solutions. An in-house build will be an exact fit to needs, but it might be difficult to get long-term management buy-in to invest in related AI technologies, compute power, storage, ongoing maintenance and a capable data science team. Off-the-shelf solutions need a lot of scrutiny to gauge if the providers are specialists who can deliver enterprise-grade chatbots. Some important considerations:

The solution should (be);

Platform & Device Agnostic so it can be built once and deployed anywhere

Have good Integration Capabilities with tools, applications and systems in the enterprise

Robust with solid security and compliance features

Versatile to handle varied use cases

Adaptable to support future scaling

Extensible to enable additional capabilities as the solution matures, and to leverage innovation to provide advanced features such as multi-language support, face recognition, integration with VR, Blockchains, IoT devices

Have a Personality! Bots with a personality add a human-touch that can be quite a differentiator. Incorporation of soft features such as natural conversational style, tone, emotion, and a dash of humor can give an edge over the competition.

About the Author:

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel and Yoga. She aspires to become a Yoga Instructor some day!

Monitoring for Success

Do you know if your end users are happy?

(In the context of users of Applications (desktop, web or cloud-based), Services, Servers and components of IT environment, directly or indirectly.)

The question may sound trivial, but it has a significant impact on the success of a company. The user experience is a journey, from the time they use the application or service, till after they complete the interaction. Experience can be determined based on factors like Speed, Performance, Flawlessness, Ease of use, Security, Resolution time, among others. Hence, monitoring the ‘Wow’ & ‘Woe’ moments of the users is vital.

Monitor is a component of GAVS’ AIOps Platform, Zero Incident FrameworkTM (ZIF). One of the key objectives of the Monitor platform is to measure and improve end-user experience. This component monitors all the layers (includes but not limited to application, database, server, APIs, end-points, and network devices) in real-time that are involved in the user experience. Ultimately,this helps to drive the environment towards Zero Incidents.

This figure shows the capability of ZIF monitoring that cut across all layers starting from end-user to storage and how it is linked to other the components of the platform

Key Features of ZIF Monitor are,

  • Unified solution for all IT environment monitoring needs: The platform covers the end-to-end monitoring of an IT landscape. The key focus is to ensure all verticals of IT are brought under thorough monitoring. The deeper the monitoring, the closer an organization is to attaining a Zero Incident EnterpriseTM.
  • Agents with self-intelligence: The intelligent agents capture various health parameters about the environment. When the target environment is already running under low resource, the agent will not task it with more load. It will collect the health-related metrics and communicate through the telemetry channel efficiently and effectively. The intelligence is applied in terms of parameters to be collected, the period of collection and many more.
  • Depth of monitoring: The core strength of Monitor is it comes with a list of performance counters which are defined by SMEs across all layers of the IT environment. This is a key differentiator; the monitoring parameters can be dynamically configured for the target environment. Parameters can be added or removed on a need basis.
  • Agent & Agentless (Remote): The customers can choose from Agent & Agentless options for the solutions. The remote solution is called as Centralized Remote Monitoring Solution (CRMS). Each monitoring parameter can be remotely controlled and defined from the CRMS. Even the agents that are running in the target environment can be controlled from the server console.
  • Compliance: Plays a key role in terms of the compliance of the environment. Compliance ranges from ensuring the availability of necessary services and processes in the target environment and defines the standard of what Application, Make, Version, Provider, Size, etc. that are allowed in the target environment.
  • Auto discovery: Monitor can auto-discover the newer elements (servers, endpoints, databases, devices, etc.) that are getting added to the environment. It can automatically add those newer elements into the purview of monitoring.
  • Auto scale: Centralized Remote Monitoring Solution (CRMS) can auto-scale on its own when newer elements are added for monitoring through auto-discovery. The auto scale includes various aspects, like load on channel, load on individual polling engine, and load on each agentless solution.
  • Real time user & Synthetic Monitoring: Real-time user monitoring is to monitor the environment when the user is active. Synthetic monitoring is through simulated techniques. It doesn’t wait for the user to make a transaction or use the system. Instead, it simulates the scenario and provide insights to make decision proactively.
  • Availability & status of devices connected: Monitor also includes the monitoring of availability and control of USB and COM port devices that are connected.
  • Black box monitoring: It is not always possible to instrument the application to get insights.Hence, the Black Box technique is used. Here the application is treated as a black box and it is monitored in terms of its interaction with the Kernel & OS through performance counters.
High level overview of Monitor’s components,

  • Agents, Agentless: These are the means through which monitoring is done at the target environment, like user devices, servers, network devices, load balancers, virtualized environment, API layers, databases, replications, storage devices, etc.
  • ZIF Telemetry Channel: The performance telemetry that are collected from source to target are passed through this channel to the big data platform.
  • Telemetry Data: Refers to the performance data and other metrics collected from all over the environment.
  • Telemetry Database:This is the big data platform, in which the telemetry data from all sources are captured and stored.
  • Intelligence Engine: This parses the telemetry data in near real time and raises notifications based on rule-based threshold and as well as through dynamic threshold.
  • Dashboard&Alerting Mechanism: These are the means through which the results of monitoring are conveyed as metrics in dashboard and as well as notifications.
  • Integration with Analyze, Predict & Remediate components: Monitoring module communicates the telemetry to Analyze & Predict components of the ZIF platform for it to use the data for analysis and apply Machine Learning for prediction. Both Monitor & Predict components, communicate with Remediate platform to trigger remediation.

The Monitor component works in tandem with Analyze, Predict and Remediate components of the ZIF platform to achieve an incident free IT environment. Implementation of ZIF is the right step to driving an enterprise towards Zero Incidents. ZIF is the only platform in the industry which comes from the single product platform owner who owns the end-to-end IP of the solution with products developed from scratch.

For more detailed information on GAVS’ Monitor, or to request a demo please visit zif.ai/products/monitor/

(To be continued…)

About the Author

Suresh Kumar Ramasamy


Suresh heads the Monitor component of ZIF at GAVS. He has 20 years of experience in Native Applications, Web, Cloud and Hybrid platforms from Engineering to Product Management. He has designed & hosted the monitoring solutions. He has been instrumental in conglomerating components to structure the Environment Performance Management suite of ZIF Monitor.

Suresh enjoys playing badminton with his children. He is passionate about gardening, especially medicinal plants.

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A Deep Dive into Deep Learning!

The Nobel Prize winner & French author André Gide said, “Man cannot discover new oceans unless he has the courage to lose sight of the shore”. This rings true with enterprises that made bold investments in cutting-edge AI that are now starting to reap rich benefits. Artificial Intelligence is shredding all perceived boundaries of a machine’s cognitive abilities. Deep Learning, at the very core of Artificial Intelligence, is pushing the envelope still further into unchartered territory. According to Gartner, “Deep Learning is here to stay and expands ML by allowing intermediate representations of the data”.

What is Deep Learning?

Deep Learning is a subset of Machine Learning that is based on Artificial Neural Networks (ANN). It is an attempt to mimic the phenomenal learning mechanisms of the human brain and train AI models to perform cognitive tasks like speech recognition, image classification, face recognition, natural language processing (NLP) and the like.

The tens of billions of neurons and their connections to each other form the brain’s neural network. Although Artificial Neural Networks have been around for quite a few decades now, they are now gaining momentum due to the declining price of storage and the exponential growth of processing power. This winning combination of low-cost storage and high computational prowess is bringing back Deep Learning from the woods.

Improved machine learning algorithms and the availability of staggering amounts of diverse unstructured data such as streaming and textual data, are boosting performance of Deep Learning systems. The performance of the ANN depends heavily on how much data it is trained with and it continuously adapts and evolves its learning with time as it is exposed to more & more datasets.

Simply put, the ANN consists of an Input layer, hidden computational layers, and the Output layer. If there is more than one hidden layer between the Input & Output layers, then it is called a Deep Network.

The Neural Network

The Neuron is central to the human Neural Network. Neurons have Dendrites, which are the receivers of information and the Axon which is the transmitter. The Axon is connected to the Dendrites of other neurons, through which signal transmission takes place. The signals that are passed are called Synapses.

While the neuron by itself cannot accomplish much, it creates magic when it forms connections with the other neurons to form an interconnected neural network. In artificial neural networks, the neuron is represented by a node or a unit. There are several interconnected layers of such units, categorized as input, output and hidden, as seen in the figure. 

A Deep Dive into Deep Learning!

The input layer receives the input values and passes them onto the first hidden layer in the ANN, similar to how our senses receive inputs from the environment around us & send signals to the brain. Let’s look at what happens in one node when it receives these input values from the different nodes of the input layer. The values are standardized/normalized-so that they are all within a certain range-and then weighted. Weights are crucial to a neural network since a value’s weight is indicative its impact on the outcome. An activation function is then applied to the weighted sum of values, to help determine if this transformed value needs to be passed on within the network. Some commonly used activation functions are the Threshold, Sigmoid and Rectifier functions.

This gives a very high-level idea of the generic structure and functioning of an ANN. The actual implementation would use one of several different architectures of neural networks that define how the layers are connected together, and what functions and algorithms are used to transform the input data. To give a couple of examples, a Convolutional network uses nonlinear activation functions and is highly efficient at processing nonlinear data like speech, image and video while a Recurrent network has information flowing around recursively, is much more complicated and difficult to train, but that much more powerful. Recurrent networks are closer in representation to the human neural network and are best suited for applications like sequence generation and predicting stock prices.

Deep Learning at work

Deep Learning has been adopted by almost all industry verticals at least at some level. To give some interesting examples, the automobile industry employs it in self-driving vehicles and driver-assistance services, the entertainment industry applies it to auto-addition of audio to silent movies and social media uses deep learning for curation of content feeds in user’s timelines. Alexa, Cortana, Google Assistant and Siri have now invaded our homes to provide virtual assistance!

Deep Learning has several applications in the field of Computer Vision, which is an umbrella term for what the computer “sees”, that is, interpreting digital visual content like images, photos or videos. This includes helping the computer learn & perform tasks like Image Classification, Object Detection, Image Reconstruction, to name a few. Image classification or image recognition when localized, can be used in Healthcare for instance, to locate cancerous regions in an x-ray and highlight them.

Deep Learning applied to Face Recognition has changed the face of research in this area. Several computational layers are used for feature extraction, with the complexity and abstraction of the learnt feature increasing with each layer, making it pretty robust for applications like public surveillance or public security in buildings. But there are still many challenges like the identification of facial features across styles, ages, poses, effects of surgery that need to be tackled before FR can be reliably used in areas like watch-list surveillance, forensic tasks which demand high levels of accuracy and low alarm rates. 

Similarly, there are several applications of deep learning for Natural Language Processing. Text Classification can be used for Spam filtering, Speech recognition can be used to transcribe a speech, or create captions for a movie, and Machine translation can be used for translation of speech and text from one language to another.

Closing Thoughts

As evident, the possibilities are endless and the road ahead for Deep Learn is exciting! But, despite the tremendous progress in Deep Learning, we are still very far from human-level AI. AI models can only perform local generalizations and adapt to new situations that are similar to past data, whereas human cognition is capable of quickly acclimatizing to radically novel circumstances. Nevertheless, this arduous R&D journey has nurtured a new-found respect for nature’s engineering miracle – the infinitely complex human brain!

Is Your Investment in TRUE AI?

Yes, AIOps the messiah of ITOps is here to stay! The Executive decision now is on the who and how, rather than when. With a plethora of products in the market offering varying shades of AIOps capabilities, choosing the right vendor is critical, to say the least.

Exclusively AI-based Ops?

Simply put, AIOps platforms leverage Big Data & AI technologies to enhance IT operations. Gartner defines Acquire, Aggregate, Analyze & Act as the four stages of AIOps. These four fall under the purview of Monitoring tools, AIOps Platforms & Action Platforms. However, there is no Industry-recognized mandatory feature list to be supported, for a Platform to be classified as AIOps. Due to this ambiguity in what an AIOps Platform needs to Deliver, huge investments made in rosy AIOps promises can lead to sub-optimal ROI, disillusionment or even derailed projects. Some Points to Ponder…

  • Quality in, Quality out. The value delivered from an AIOps investment is heavily dependent on what data goes into the system. How sure can we be that IT Asset or Device monitoring data provided by the Customer is not outdated, inaccurate or patchy? How sure can we be that we have full visibility of the entire IT landscape? With Shadow IT becoming a tacitly approved aspect of modern Enterprises, are we seeing all devices, applications and users? Doesn’t this imply that only an AIOps Platform providing Application Discovery & Topology Mapping, Monitoring features would be able to deliver accurate insights?
  • There is a very thin line between Also AI and Purely AI. Behind the scenes, most AIOps Platforms are reliant on CMDB or similar tools, which makes Insights like Event Correlation, Noise Reduction etc., rule-based. Where is the AI here?
  • In Gartner’s Market Guide, apart from support features for the different data types, Automated Pattern Discovery is the only other Capability taken into account for the Capabilities of AIOps Vendors matrix. With Gartner being one of the most trusted Technology Research and Advisory companies, it is natural for decision makers to zero-in on one of these listed vendors. What is not immediately evident is that there is so much more to AIOps than just this, and with so much at stake, companies need to do their homework and take informed decisions before finalizing their vendor.
  • Most AIOps vendors ingest, provide access to & store heterogenous data for analysis, and provide actionable Insights and RCA; at which point the IT team takes over. This is a huge leap forward, since it helps IT work through the data clutter and significantly reduces MTTR. But, due to the absence of comprehensive Predictive, Prescriptive & Remediation features, these are not end-to-end AIOps Platforms.
  • At the bleeding edge of the Capability Spectrum is Auto-Remediation based on Predictive & Prescriptive insights. A Comprehensive end-to-end AIOps Platform would need to provide a Virtual Engineer for Auto-Remediation. But, this is a grey area not fully catered to by AIOps vendors.  

The big question now is, if an AIOps Platform requires human intervention or multiple external tools to take care of different missing aspects, can it rightfully claim to be true end-to-end AIOps?

So, what do we do?

Time for you to sit back and relax! Introducing ZIF- One Solution for all your ITOps ills!

We have you completely covered with the full suite of tools that an IT infrastructure team would need. We deliver the entire AIOps Capability spectrum and beyond.

ZIF (Zero Incident Framework™) is an AIOps based TechOps platform that enables proactive Detection and Remediation of incidents helping organizations drive towards a Zero Incident Enterprise™.

The Key Differentiator is that ZIF is a Pure-play AI Platform powered by Unsupervised Pattern-based Machine Learning Algorithms. This is what sets us a Class Apart.

  • Rightly aligns with the Gartner AIOps strategy. ZIF is based on and goes beyond the AIOps framework
  • Huge Investments in developing various patented AI Machine Learning algorithms, Auto-Discovery modules, Agent & Agentless Application Monitoring tools, Network sniffers, Process Automation, Remediation & Orchestration capabilities to form Zero Incident Framework™
  • Powered entirely by Unsupervised Pattern-based Machine Learning Algorithms, ZIF needs no further human intervention and is completely Self-Reliant
  • Unsupervised ML empowers ZIF to learn autonomously, glean Predictive & Prescriptive Intelligence and even uncover Latent Insights
  • The 5 Modules can work together cohesively or as independent stand-alone components
  • Can be Integrated with existing Monitoring and ITSM tools, as required
  • Applies LEAN IT Principle and is on an ambitious journey towards FRICTIONLESS IT.

Realizing a Zero Incident EnterpriseTM

AIOps Demystified

IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.

As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.

The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.

Why AI in IT operations?

The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.

Visibility

The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.

The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.

Monitoring

When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.

The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.

Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).

Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.

Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).

Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.

Advanced Analytics

Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.

Remediation

AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.

Conclusion

The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.

GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.

READ ALSO OUR NEW UPDATES

AIOps Demystified

IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.

As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.

The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.

Why AI in IT operations?

The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.

Visibility

The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.

The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.

Monitoring

When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.

The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.

Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).

Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.

Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).

Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.

Advanced Analytics

Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.

Remediation

AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.

Conclusion

The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.

GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.

READ ALSO OUR NEW UPDATES