Tag: alert correlation machine learning services

How ZIF Delivers Service Reliability to Financial Institutions Using AIOps

Posted on by Subhadeep Datta

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! 

ZIF Offers a Resilient IT Cure to the Healthcare Sector

Posted on by Subhadeep Datta

The healthcare sector is going through a paradigm shift as more and more facilities are undergoing digital transformation. The use of new-age technologies like AI and ML have boosted the productivity of healthcare facilities. Healthcare facilities are now focusing on implementing an organized IT infrastructure. Besides offering products and services, the healthcare industry is also involved in finance processes. To manage these components of the healthcare sector, a robust IT framework can be established. Read on to know how ZIF can offer a resilient IT cure to the healthcare sector.

What is ZIF?

ZIF (Zero Incidence Framework) is an AI-based framework distributed by GAVS Technologies. It is an AIOps (Artificial Intelligence for IT Operations) platform. AIOps platforms are used for inducing automation and resilience in the IT infrastructure. AIOps products use AI and ML to reduce the number of incidents in the IT infrastructure.

ZIF can help you in discovering business applications in your environment. It helps in monitoring the performance of digital interfaces in your environment. You can not only set a reliable IT infrastructure but can also make it resilient. Your IT tools and technologies will be able to recover quickly from any outage/failure with ZIF.

Artificial intelligence in the healthcare industry

Before choosing ZIF for your healthcare facility, you should be aware of the use cases of artificial intelligence. The uses of AI-based platforms in the healthcare industry are as follows:

  • AI is being used for medical imaging by healthcare facilities.
  • AI is used for drug discovery.
  • AI-based platforms are used by healthcare facilities for better IT infrastructure.  
  • AI helps in automating cybersecurity processes in the healthcare sector.
  • AI is used to create virtual health assistants.

ZIF for IT infrastructure in the healthcare sector

Healthcare facilities run critical enterprise applications that are responsible for patient care. If the performance of such critical applications downgrades, it will harm the reliability of the healthcare facility. The IT landscape has changed a lot over the years and, healthcare facilities are finding it hard to keep up. Most healthcare facilities do not hire IT experts and use premade IT frameworks for patient care. The premade frameworks often fail when they experience more load and traffic. All these challenges can be solved by using ZIF for a robust IT infrastructure in your healthcare facility.

ZIF is a reliable AIOps solution that can help you in eliminating risks and incidents from your IT infrastructure. ZIF works on an unsupervised learning model and does not needs more manual efforts. With the growing needs of a healthcare facility, ZIF can help the IT infrastructure to scale up. Your workers can focus on treating the patients while ZIF can handle the service reliability of your digital solutions.

System reliability with ZIF

The healthcare software systems are very sensitive and, a slight mishap can cause a big blunder. The Healthcare industry has to learn from past system failures to make sure it never happens. Healthcare systems should be safe and reliable to provide the best results. Healthcare organizations often face challenges while upgrading their software systems according to the requirements. System reliability in healthcare is measured in terms of failure-free operation of software systems.

ZIF will help you in ensuring that digital systems operate without any failures over time. It will continuously check for any issues with software systems. Once an incidence is reported, ZIF will help you in eliminating it as soon as possible. It will help you in enhancing system reliability and uptime for your healthcare facility.

Enhanced monitoring with ZIF

Due to the recent COVID pandemic, healthcare organizations have started monitoring the health of patients remotely. For online advisory and telemedicine, healthcare facilities have to deploy the required systems. They need to have reliable systems that connect them to the patients. For the continuous performance of these systems, they are connected to a central monitoring system. If the monitoring system is not able to detect the reason for the poor performance of other systems, it may harm the patient’s health.

With ZIF, you can monitor the health of all the consolidated systems under one dashboard. The OEM device monitoring feature of ZIF lets you analyze the health of digital systems anytime. ZIF is a reliable AIOps tool that can let you set thresholds for the maintenance of digital systems. ZIF also provides a consolidated view of your organizational data for high-end analytics. The monitoring of all digital systems via ZIF can significantly increase service efficiency.

Reliability prediction with ZIF

ZIF not only solves the current incidences but also predicts future incidences. ZIF will evaluate the performance of systems and will predict their future failure chances. ZIF will provide you with a failure rate that can define the vulnerability of a system. It will let you make proactive approaches to eliminating future failure chances. You can create resilient IT systems with ZIF for your healthcare facility. Resilient IT systems quickly recover after an incidence and provide effective performance over time.

Autonomous IT systems with ZIF

Do you want your staff members to focus more on patient service than system monitoring? Well, ZIF will help you in automating various day-to-day IT operations. You can set automated responses for a particular type of incidence via ZIF. It is an AIOps solution specifically designed for autonomous and predictive IT processes.

ZIF will monitor user experience and identify latencies and anomalies in real-time. This process will be done automatically by ZIF without any manual efforts. Even if the end-user is not able to identify any anomaly, ZIF will find it out. You can configure ZIF to send automated and real-time alerts for any incidence. It will also provide the SOP for incidence protection in real-time.

In a nutshell

The global AIOps market size will reach around USD 20 billion by 2025. AIOps platforms for healthcare can help them undergo digital transformation quickly. ZIF can help you with device monitoring and enhancing system resiliency. Choose ZIF for system reliability and resiliency!

Large Language Models: A Leap in the World of Language AI

Posted on by Subhadeep Datta

In Google’s latest annual developer conference, Google I/O, CEO Sundar Pichai announced their latest breakthrough called “Language Model for Dialogue Applications” or LaMDA. LaMDA is a language AI technology that can chat about any topic. That’s something that even a normal chatbot can do, then what makes LaMDA special?

Modern conversational agents or chatbots follow a narrow pre-defined conversational path, while LaMDA can engage in a free-flowing open-ended conversation just like humans. Google plans to integrate this new technology with their search engine as well as other software like voice assistant, workplace, gmail, etc. so that people can retrieve any kind of information, in any format (text, visual or audio), from Google’s suite of products. LaMDA is an example of what is known as a Large Language Model (LLM).

Introduction and Capabilities

What is a language model (LM)? A language model is a statistical and probabilistic tool that determines the probability of a given sequence of words occurring in a sentence. Simply put, it is a tool that is trained to predict the next word in a sentence. It works like how a text message autocompletes works. Where weather models predict the 7-day forecast, language models try to find patterns in the human language, one of computer science’s most difficult puzzles as languages are ever-changing and adaptable.

A language model is called a large language model when it is trained on enormous amount of data. Some of the other examples of LLMs are Google’s BERT and OpenAI’s GPT-2 and GPT-3. GPT-3 is the largest language model known at the time with 175 billion parameters trained on 570 gigabytes of text. These models have capabilities ranging from writing a simple essay to generating complex computer codes – all with limited to no supervision.

Limitations and Impact on Society

As exciting as this technology may sound, it has some alarming shortcomings.

1. Biasness: Studies have shown that these models are embedded with racist, sexist, and discriminatory ideas. These models can also encourage people for genocide, self-harm, and child sexual abuse. Google is already using an LLM for its search engine which is rooted in biasness. Since Google is not only used as a primary knowledge base for general people but also provides an information infrastructure for various universities and institutions, such a biased result set can have very harmful consequences.

2. Environmental impact: LLMs also have an outsize impact on the environment as these emit shockingly high carbon dioxide – equivalent to nearly five times the lifetime emissions of an average car including manufacturing of the car.

3. Misinformation: Experts have also warned about the mass production of misinformation through these models as because of the model’s fluency, people can confuse into thinking that humans have produced the output. Some models have also excelled at writing convincing fake news articles.

4. Mishandling negative data: The world speaks different languages that are not prioritized by Silicon Valley. These languages are unaccounted for in the mainstream language technologies and hence, these communities are affected the most. When a platform uses an LLM which is not capable of handling these languages to automate its content moderation, the model struggles to control the misinformation. During extraordinary situations, like a riot, the amount of unfavorable data coming in is huge, and this ends up creating a hostile digital environment. The problem does not end here. When the fake news, hate speech, and all such negative text is not filtered, it is used as training data for the next generation of LLMs. These toxic linguistic patterns then parrot back on the internet.

Further Research for Better Models

Despite all these challenges, very little research is being done to understand how this technology can affect us or how better LLMs can be designed. In fact, the few big companies that have the required resources to train and maintain LLMs refuse or show no interest in investigating them. But it’s not just Google that is planning to use this technology. Facebook has developed its own LLMs for translation and content moderation while Microsoft has exclusively licensed GPT-3. Many startups have also started creating products and services based on these models.

While the big tech giants are trying to create private and mostly inaccessible models that cannot be used for research, a New York-based startup, called Hugging Face, is leading a research workshop to build an open-source LLM that will serve as a shared resource for the scientific community and can be used to learn more about the capabilities and limitations of these models. This one-year-long research (from May 2021 to May 2022) called the ‘Summer of Language Models 21’ (in short ‘BigScience’) has more than 500 researchers from around the world working together on a volunteer basis.

The collaborative is divided into multiple working groups, each investigating different aspects of model development. One of the groups will work on calculating the model’s environmental impact, while another will focus on responsible ways of sourcing the training data, free from toxic language. One working group is dedicated to the model’s multilingual character including minority language coverage. To start with, the team has selected eight language families which include English, Chinese, Arabic, Indic (including Hindi and Urdu), and Bantu (including Swahili).

Hopefully, the BigScience Project will help produce better tools and practices for building and deploying LLMs responsibly. The enthusiasm around these large language models cannot be curbed but it can surely be nudged in a direction that has lesser shortcomings. Soon enough, all our digital communications—be it emails, search results, or social media posts —will be filtered using LLMs. These large language models are the next frontier for artificial intelligence.

References

About the Author –

Priyanka Pandey

Priyanka is a software engineer at GAVS with a passion for content writing. She is a feminist and is vocal about equality and inclusivity. She believes in the cycle of learning, unlearning and relearning. She likes to spend her free time baking, writing and reading articles especially about new technologies and social issues.

#EmpathyChallenge – 3 Simple Ways to Practice Empathy Consciously

Posted on by Subhadeep Datta

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.

References

Padma Ravichandran

About the Author –

Padma is intrigued by Organization Culture and Behavior at workplace that impact employee experience. She is also passionate about driving meaningful initiatives for enabling women to Lean In, along with her fellow Sheroes. She enjoys reading books, journaling, yoga and learning more about life through the eyes of her 8-year-old son.

Why is AIOps an Industrial Benchmark for Organizations to Scale in this Economy?

Posted on by ZIF

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:

  1. High Application Availability & Reliability
  2. Optimized Performance Tuning & Monitoring
  3. Operational gains & Cost Optimization
  4. Generation of Actionable Insights for Efficiency
  5. 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.

ai automated root cause analysis solution

While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:

  1. Understanding the feasibility and concerns for its future adoption
  2. Classification of business processes and use cases for AIOps intervention
  3. 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.

About the Author –

Ashish Joseph

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs a series called #BizPective on LinkedIn and Instagram focusing on contemporary business trends from a different perspective. Outside work, he is very passionate about basketball, music and food.

Algorithmic Alert Correlation

Posted on by ZIF

Today’s always-on businesses and 24×7 uptime demands have necessitated IT monitoring to go into overdrive. While constant monitoring is a good thing, the downside is that the flood of alerts generated can quickly get overwhelming. Constantly having to deal with thousands of alerts each day causes alert fatigue, and impacts the overall efficiency of the monitoring process.

Hence, chalking out an optimal strategy for alert generation & management becomes critical. Pattern-based thresholding is an important first step, since it tunes thresholds continuously, to adapt to what ‘normal’ is, for the real-time environment. Threshold accuracy eliminates false positives and prevents alerts from getting fired incorrectly. Selective alert suppression during routine IT Ops maintenance activities like backups, patches, or upgrades, is another. While there are many other strategies to keep alert numbers under control, a key process in alert management is the grouping of alerts, known as alert correlation. It groups similar alerts under one actionable incident, thereby reducing the number of alerts to be handled individually.

But, how is alert ‘similarity’ determined? One way to do this is through similarity definitions, in the context of that IT landscape. A definition, for instance, would group together alerts generated from applications on the same host, or connectivity issues from the same data center. This implies that similarity definitions depend on the physical and logical relationships in the environment – in other words – the topology map. Topology mappers detect dependencies between applications, processes, networks, infrastructure, etc., and construct an enterprise blueprint that is used for alert correlation.

But what about related alerts generated by entities that are neither physically nor logically linked? To give a hypothetical example, let’s say application A accesses a server S which is responding slowly, and so A triggers alert A1. This slow communication of A with S eats up host bandwidth, and hence affects another application B in the same host. Due to this, if a third application C from another host calls B, alert A2 is fired by C due to the delayed response from B.  Now, although we see the link between alerts A1 & A2, they are neither physically nor logically related, so how can they be correlated? In reality, such situations could imply thousands of individual alerts that cannot be combined.

Algorithmic Alert Correlation

This is one of the many challenges in IT operations that we have been trying to solve at GAVS. The correlation engine of our AIOps Platform ZIF uses algorithmic alert correlation to find a solution for this problem. We are working on two unsupervised machine learning algorithms that are fundamentally different in their approach – one based on pattern recognition and the other based on spatial clustering. Both algorithms can function with or without a topology map, and work around what is supplied and available. The pattern learning algorithm derives associations based on learnings from historic patterns of alert relationships. The spatial clustering algorithm works on the principle of similarity based on multiple features of alerts, including problem similarity derived by applying Natural Language Processing (NLP), and relationships, among several others. Tuning parameters enable customization of algorithmic behavior to meet specific demands, without requiring modifications to the core algorithms. Time is also another important dimension factored into these algorithms, since the clustering of alerts generated over an extended period of time will not give meaningful results.

Traditional alert correlation has not been able to scale up to handle the volume and complexity of alerts generated by the modern-day hybrid and dynamic IT infrastructure. We have reached a point where our ITOps needs have surpassed the limits of human capabilities, and so, supplementing our intelligence with Artificial Intelligence and Machine Learning has now become indispensable.

About the Authors –

Padmapriya Sridhar

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 someday!

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Generative Adversarial Networks (GAN)

Posted on by ZIF

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.

Generative Adversarial Networks

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.

Generative Adversarial Networks

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.

Generative Adversarial Networks

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.

About the Author –

Naresh B

Naresh is a part of Location Zero at GAVS as an AI/ML solutions developer. His focus is on solving problems leveraging AI/ML.
He strongly believes in making success as a habit rather than considering it as a destination.
In his free time, he likes to spend time with his pet dogs and likes sketching and gardening.

Assess Your Organization’s Maturity in Adopting AIOps

Posted on by ZIF

Artificial Intelligence for IT operations (AIOps) is adopted by organizations to deliver tangible Business Outcomes. These business outcomes have a direct impact on companies’ revenue and customer satisfaction.

A survey from AIOps Exchange 2019, reports that 84% of Business Owners who attended the survey, confirmed that they are actively evaluating AIOps to be adopted in their organizations.

So, is AIOps just automation? Absolutely NOT!!

Artificial Intelligence for IT operations implies the implementation of true Autonomous Artificial Intelligence in ITOps, which needs to be adopted as an organization-wide strategy. Organizations will have to assess their existing landscape, processes, and decide where to start. That is the only way to achieve the true implementation of AIOps.

Every organization trying to evaluate AIOps as a strategy should read through this article to understand their current maturity, and then move forward to reach the pinnacle of Artificial Intelligence in IT Operations.

The primary Success Factor in adopting AIOps is derived from the Business Outcomes the organization is trying to achieve by implementing AIOps –that is the only way to calculate ROI.

There are 4 levels of Maturity in AIOps adoption. Based on our experience in developing an AIOps platform and implementing the platform across multiple industries, we have arrived at these 4 levels. Assessing an organization against each of these levels helps in achieving the goal of TRUE Artificial Intelligence in IT Operations.

Level 1: Knee-jerk

Events, logs are generated in silos and collected from various applications and devices in the infrastructure. These are used to generate alerts that are commissioned to command centres to escalate as per the SOPs (standard operating procedures) defined. The engineering teams work in silos, not aware of the business impact that these alerts could potentially create. Here, operations are very reactive which could cost the organization millions of dollars.

Level 2: Unified

Have integrated all events, logs, and alerts into one central locale. ITSM process has been unified. This helps in breaking silos and engineering teams are better prepared to tackle business impacts. SOPs have been adjusted since the process is unified, but this is still reactive incident management.

Level 3: Intelligent

Machine Learning algorithms (either supervised or unsupervised) have been implemented on the unified data to derive insights. There are baseline metrics that are calibrated and will be used as a reference for future events. With more data, the metrics get richer. IT operations team can correlate incidents/events with business impacts by leveraging AI & ML. If Mean Time To Resolve (MTTR) an incident has been reduced by automated identification of the root cause, then the organization has attained level 3 maturity in AIOps.

Level 4: Predictive & Autonomous

The pinnacle of AIOps is level 4. If incidents and performance degradation of applications can be predicted by leveraging Artificial Intelligence, it implies improved application availability. Autonomousremediation bots can be triggered spontaneously based on the predictive insights, to fix incidents that are prone to happen in the enterprise. Level 4 is a paradigm shift in IT operations – moving operations entirely from being reactive, to becoming proactive.

Conclusion:

As IT operations teams move up each level, the essential goal to keep in mind is the long-term strategy that needs to be attained by adopting AIOps. Artificial Intelligence has matured over the past few decades, and it is up to AIOps platforms to embrace it effectively. While choosing an AIOps platform, measure the maturity of the platform’s artificial intelligent coefficient.

About the Author:

Anoop Aravindakshan (Principal Consultant Manager) at GAVS Technologies.


An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.

Prediction for Business Service Assurance

Posted on by ZIF

Artificial Intelligence for IT operations or AIOps has exploded over the past few years. As more and more enterprises set about their digital transformation journeys, AIOps becomes imperative to keep their businesses running smoothly. 

AIOps uses several technologies like Machine Learning and Big Data to automate the identification and resolution of common Information Technology (IT) problems. The systems, services, and applications in a large enterprise produce volumes of log and performance data. AIOps uses this data to monitor the assets and gain visibility into the behaviour and dependencies among these assets.

According to a Gartner publication, 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.

Let us understand more about thePredict module of ZIF.

Predictive Analytics is one of the main USP of the ZIF platform. ZIF encompassesSupervised, Unsupervised and Reinforcement Learning algorithms for realization of various business use cases (as shown below).

How does the Predict Module of ZIF work?

Through its data ingestion capabilities, the ZIF platform can receive and process all types of data (both structured and unstructured) from various tools in the enterprise. The types of data can be related to alerts, events, logs, performance of devices, relations of devices, workload topologies, network topologies etc. By analyzing all these data, the platform predicts the anomalies that can occur in the environment. These anomalies get presented as ‘Opportunity Cards’ so that suitable action can be taken ahead of time to eliminate any undesired incidents from occurring. Since this is ‘Proactive’ and not ‘Reactive’, it brings about a paradigm shift to any organization’s endeavour to achieve 100% availability of their enterprise systems and platforms. Predictions are done at multiple levels – application level, business process level, device level etc.

Sub-functions of Prediction Module

How does the Predict module manifest to enterprise users of the platform?

Predict module categorizes the opportunity cards into three swim lanes.

  1. Warning swim lane – Opportunity Cards that have an “Expected Time of Impact” (ETI) beyond 60 minutes.
  2. Critical swim lane – Opportunity Cards that have an ETI within 60 minutes.
  3. Processed / Lost– Opportunity Cards that have been processed or lost without taking any action.

Few of the enterprises that realized the power of ZIF’s Prediction Module

  • A manufacturing giant in the US
  • A large non-profit mental health and social service provider in New York
  • A large mortgage loan service provider in the US
  • Two of the largest private sector banks in India

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

References:https://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.

Discover, Monitor, Analyze & Predict COVID-19

Posted on by ZIF

“Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. Netflix, the world’s largest movie house, own no cinemas. And Airbnb, the world’s largest accommodation provider, owns no real estate. Something interesting is happening.”

– Tom Goodwin, an executive at the French media group Havas.

This new breed of companies is the fastest growing in history because they own the customer interface layer. It is the platform where all the value and profit is. “Platform business” is a more wholesome termfor this model for which data is the fuel; Big Data & AI/ML technologies are the harbinger of new waves of productivity growth and innovation.

With Big data and AI/ML is making a big difference in the area of public health, let’s see how it is helping us tackle the global emergency of coronavirus formally known as COVID-19.

“With rapidly spreading disease, a two-week lag is an eternity.”

DISCOVERING/ DETECTING

Chinese technology giant Alibaba has developed an AI system for detecting the COVID-19 in CT scans of patients’ chests with 96% accuracy against viral pneumonia cases. It only takes 20 seconds for the AI to decide, whereas humans generally take about 15 minutes to diagnose the illness as there can be upwards of 300 images to evaluate.The system was trained on images and data from 5,000 confirmed coronavirus cases and has been tested in hospitals throughout China. Per a report, at least 100 healthcare facilities are currently employing Alibaba’s AI to detect COVID-19.

Ping An Insurance (Group) Company of China, Ltd (Ping An) aims to address the issue of lack of radiologists by introducing the COVID-19 smart image-reading system. This image-reading system can read the huge volumes of CT scans in epidemic areas.

Ping An Smart Healthcare uses clinical data to train the AI model of the COVID-19 smart image-reading system. The AI analysis engine conducts a comparative analysis of multiple CT scan images of the same patient and measures the changes in lesions. It helps in tracking the development of the disease, evaluation of the treatment and in prognosis of patients.Ultimately it assists doctors to diagnose, triage and evaluate COVID-19 patients swiftly and effectively.

Ping An Smart Healthcare’s COVID-19 smart image-reading system also supports AI image-reading remotely by medical professionals outside the epidemic areas.Since its launch, the smart image-reading system has provided services to more than 1,500 medical institutions. More than 5,000 patients have received smart image-reading services for free.

The more solutions the better. At least when it comes to helping overwhelmed doctors provide better diagnoses and, thus, better outcomes.

MONITORING

  • AI based Temperature monitoring & scanning

In Beijing, China, subway passengers are being screened for symptoms of coronavirus, but not by health authorities. Instead, artificial intelligence is in-charge.

Two Chinese AI giants, Megvii and Baidu, have introduced temperature-scanning. They have implemented scanners to detect body temperature and send alerts to company workers if a person’s body temperature is high enough to constitute a fever.

Megvii’s AI system detects body temperatures for up to 15 people per second andup to 16 feet. It monitors as many as 16 checkpoints in a single station. The system integrates body detection, face detection, and dual sensing via infrared cameras and visible light. The system can accurately detect and flag high body temperature even when people are wearing masks, hats, or covering their faces with other items. Megvii’s system also sends alerts to an on-site staff member.

Baidu, one of the largest search-engine companies in China, screens subway passengers at the Qinghe station with infrared scanners. It also uses a facial-recognition system, taking photographs of passengers’ faces. If the Baidu system detects a body temperature of at least 99-degrees Fahrenheit, it sends an alert to the staff member for another screening. The technology can scan the temperatures of more than 200 people per minute.

  • AI based Social Media Monitoring

An international team is using machine learning to scour through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs of the virus across geographies.The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel.

The approach has proven capable of spotting a coronavirus needle in a haystack of big data. This technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk quicker than using official medical sources.

PREDICTING

Data from hospitals, airports, and other public locations are being used to predict disease spread and risk. Hospitals can also use the data to plan for the impact of an outbreak on their operations.

Kalman Filter

Kalman filter was pioneered by Rudolf Emil Kalman in 1960, originally designed and developed to solve the navigation problem in the Apollo Project. Since then, it has been applied to numerous cases such as guidance, navigation, and control of vehicles, computer vision’s object tracking, trajectory optimization, time series analysis in signal processing, econometrics and more.

Kalman filter is a recursive algorithm which uses time-series measurement over time, containing statistical noise and produce estimations of unknown variables.

For the one-day prediction Kalman filter can be used, while for the long-term forecast a linear model is used where its main features are Kalman predictors, infected rate relative to population, time-depended features, and weather history and forecasting.

The one-day Kalman prediction is very accurate and powerful while a longer period prediction is more challenging but provides a future trend.Long term prediction does not guarantee full accuracy but provides a fair estimation following the recent trend. The model should re-run daily to gain better results.

GitHub Link: https://github.com/Rank23/COVID19

ANALYZING

The Center for Systems Science and Engineering at Johns Hopkins University has developed an interactive, web-based dashboard that tracks the status of COVID-19 around the world. The resource provides a visualization of the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries.

The primary data source for the tool is DXY, a Chinese platform that aggregates local media and government reports to provide COVID-19 cumulative case totals in near real-time at the province level in China and country level otherwise. Additional data comes from Twitter feeds, online news services and direct communication sent through the dashboard. Johns Hopkins then confirms the case numbers with regional and local health departments. This kind of Data analytics platform plays a pivotal role in addressing the coronavirus outbreak.

All data from the dashboard is also freely available in the following GitHub repository.

GitHub Link:https://bit.ly/2Wmmbp8

Mobile version: https://bit.ly/2WjyK4d

Web version: https://bit.ly/2xLyT6v

Conclusion

One of AI’s core strengths when working on identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. AIsystems never tire, can sift through enormous amounts of data, and identify possible correlations and causations that humans can’t.

However, there are limits to AI’s ability to both identify virus outbreaks and predict how they will spread. Perhaps the best-known example comes from the neighboring field of big data analytics. At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu—until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest.Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can wreak havoc on the prediction power of AI.

References:

About the Author:

Bargunan Somasundaram

Bargunan Somasundaram

Bargunan is a Big Data Engineer and a programming enthusiast. His passion is to share his knowledge by writing his experiences about them. He believes “Gaining knowledge is the first step to wisdom and sharing it is the first step to humanity.”