IT operations deal with a huge volume of data every day which puts tremendous pressure on the IT workforce. In addition, this also results in loss of optimization and real-time monitoring and resolution of issues. Artificial Intelligence in IT Operations or AIOps, efficiently handles these basic tasks to reduce the burden on IT operations and automates basic functions like monitoring, service desk, technical support amongst other solutions.
What is AIOps and how it can help your company?
AIOps works on three aspects: monitoring, engaging, and acting on big data. AIOps basically includes the application of machine learning and big data in IT operations. AIOps not just benefits IT and cloud-based companies but also see implementation in healthcare, finance, insurance, and other sectors.
Some use cases of AIOps include:
- Automation tools for service desk
- Realtime user monitoring tools
- Application performance monitoring
- Ingestion of data to recognize events and remediation
AIOps monitors data across IT systems, devices, and processes and helps companies control the ace at the following:
- Root-cause Analysis
- Anomaly Detection
- Realtime Notification
- Automated Event Management
- Dependency Mapping
This results in reduced costs for companies and less reliance on the human workforce. It also helps in scaling down errors and increasing the productivity of the workforce by organizing shifts for a smooth experience. It offers service reliability as the tool can be operated 24/7.
For example, AIOps in healthcare can easily replace the helpdesk and takeover booking appointments, generating triggers for issues, flagging important and emergency requests along with assigning them to the relevant teams. The prediction and remediation of issues can be a game-changer in the healthcare industry.
How to choose the right AIOps tool for your business
While the application of AIOps is very beneficial for a company, the implementation of the right tool is critical. So how do you identify the best AIOps solution for your business? All AIOps tools do not fit every business. Choosing the right tool depends on a match between your company’s IT goals and the features offered by the AIOps tool. The suitability of the two will determine whether an AIOps product works for your company.
Here are some factors to consider while picking an AIOps tool
- Complexity – The first factor is the level of complexity involved in your business. Higher complex environments require expensive AIOps to be deployed with better features. Understand what kind of features and functions are helpful for your business before implementing an AIOPs tool for your business. AIOps do not reduce complexity but give the company a tool to deal with large sets of data and process it in real-time for better decision making.
- Monitoring – The monitoring features of an AIOps tool are critical while selecting the right tool. However, it is not limited to only monitoring. A tool cannot entirely be considered AIOps if it offers only storage and retrieval of data.
- Connectivity – Connectivity to systems varies for every company and finding an AIOPs tool that offers connectivity to systems like Kubernetes, SAP and others is important. It isn’t easy to deploy such connectivity on your own. It is easy to determine what kind of connectivity your business needs. The factors involved include connectivity to a system and the ability to gather data while controlling that system.
- Return on investment – To measure returns on AIOps, you need historical data and monitor the progress. Typically, the ROI can be measured within 6 months of deployment. The result may not yield 100% results, but it definitely offers increased efficiency. One must also take into consideration the time taken to resolve issues using the human workforce to measure the value of your investment.
- Observability – Through observability, companies can monitor internal systems and use predictive analytics models to find anomalies and detect issues. After detection, the companies can then administer resolution and remediation of such issues. It also helps companies in being proactive in finding solutions for issues and predicting and detecting abnormal behaviors.
- Root-cause analysis – To know the origin of a failure or issue is one of the main features that help businesses trace and remedy an incident or event. Root-cause analysis helps businesses understand the primary cause even in complex and interdependent systems. AIOps tools that provide this feature, help companies that have multi-dependent and interwoven systems.
- Automation features – It is not enough for many businesses to ingest, correlate and understand data, events, and anomalies. The deployment of automated remedies not just saves time and effort but also reduces the costs involved. Automation features replace manual labor and save on human resource costs. It also helps in 24/7 monitoring and resolution which is beneficial to both the company and customers.
Choosing the right AIOps tools varies from company to company as their IT operating systems and requirements are different. However, understanding what your IT infrastructure needs, charting your AIOps transformation journey, and aligning it with your business goals can help you pick the right tool for your business.
Would you like an automatic computer update in the middle of booking the only available plane ticket? Imagine that in the context of an organization. While updating or maintaining a software system, the whole IT infrastructure should not come to a standstill. Organizations must ensure service availability while updating or maintaining their software systems. For adding a new microservice, organizations cannot shut down the entire IT system as it would affect service reliability. This is where containers and Kubernetes come into action.
Due to the recent COVID pandemic, the demand for virtual desktop infrastructure solutions has increased. While virtual machines are helpful in the current remote working culture, there are issues with deploying multiple applications. If multiple applications are deployed on a VDI desktop virtualization software, changes to shared dependencies can cause system failures. In order to not compromise with their service availability, firms decided to deploy only one application per virtual machine. However, as evident, a firm using multiple applications cannot use too many virtual machines due to cost constraints.
Containers were introduced to solve the problem of conflicting dependencies while deploying applications on virtual machines. Each container has its own storage, processing power, CPU, and file systems. Since a container has its own operating system, it can be easily decoupled from other applications on a virtual machine. You do not have to affect your service availability each time for adding a new application to your VDI desktop virtualization software. It can run anything from a small microservice to a large application.
Kubernetes (K8s) is an open-source platform for managing the deployment of applications in containers. Launched by Google, K8s can help you run applications on virtual machines without affecting the service availability. The process of managing groups of containers is known as orchestration in the IT world. The functionalities of Kubernetes are as follows:
- K8s decide the appropriate place to deploy your containers by analyzing their resource needs.
- K8s always have a backup container if any container crashes during deployment.
- K8s can manipulate the number of instances based on the CPU requirements.
- The non-volatile storage used by applications inside containers can be managed by K8s.
- K8s are responsible for load balancing of IP address and DNS.
- During an update, K8s closely monitor the health of the instances that are being introduced. If the update crashes, K8s help in restoring the previous version immediately without hampering the service availability.
Why Use Kubernetes?
Kubernetes has many positives:
- Kubernetes is highly portable allowing IT teams to deploy newer applications easily. Firms do not have to change the architecture of their IT infrastructure for adding a new application to virtual machines.
- Besides virtual machines, you can use K8s for deploying containers on cloud environments. With several use cases, IT teams can scale much faster without hampering service reliability.
- K8s is open-source and comes with its cost benefits.
- K8s offer enhanced availability enabling organizations to improve their service availability.
Breaking down the Architecture of K8s
Kubernetes follows the master-slave architecture as it has one master and multiple worker nodes. The master and worker nodes of K8s are explained below:
- Kubernetes Master – For a collection of servers, Kubernetes Master is the central controlling unit. Across each cluster, the networking and communication aspect is managed by the Kubernetes Master. It uses an API server that manages requests from various worker nodes. It also consists of a controller manager to maintain the shared state of a group of servers. It is the main reason why K8s ensure service availability at all times. Other components of Kubernetes Master are Etcd storage and Kubernetes scheduler.
- Worker Nodes – Kubernetes Master decides the workload of various worker nodes. The worker nodes consist of Kubelet that is responsible for monitoring the health of containers. If a worker node fails during deployment, another healthy pod is launched immediately to maintain the service availability. A pod is the structural unit of K8s that represents the workloads that are to be deployed.
Why AIOps is being used with Containers?
AIOps (Artificial Intelligence for IT Operations) is known for its application performance monitoring capabilities. However, organizations are using Kubernetes with an AIOps based analytics platform to achieve better results. An AIOps based analytics platform will offer high observability inside containers. IT teams can correlate the data generated by Kubernetes and system alerts to find the root cause of a particular IT incident. Besides managing current issues with the deployment of containers, an AIOps based analytics platform will also help you in identifying future issues.
In a nutshell
The global Kubernetes solutions market has grown in recent years. The AIOps global market worth is also growing and will be around USD 20 billion by the end of 2025. Start using Kubernetes and AIOps to boost your service availability!
Artificial Intelligence and Machine Learning (AI/ML) form the core of GAVS’ core product ZIFTM. It ingests structured and unstructured data from IT infrastructure such as networks, devices, servers, etc., and provides topology, reduces noise, predicts downtime, and helps remediate. Our healthcare and non-healthcare clients have seen immense value through ZIFTM with CAPEX and OPEX reductions of up to 30% and 60% respectively.
Artificial Intelligence (AI) has become an inevitable tool of innovation in healthcare, progressing from being a nice-to-have to a must-have. It is believed that by using AI/ML solutions and applications, healthcare organizations can diagnose diseases more precisely, prevent illnesses more proactively, treat illnesses more effectively, and reduce cost through operational optimizations. According to several industry reports, the AI healthcare market is poised to reach USD 61 billion by 2027.
GAVS believes in harvesting the power of Advanced Analytics and AI for smart healthcare operations. GAVS’ Artificial Intelligence-as-a-Service (AIaaS) is focused on leveraging AI to improve the holistic health of patients and to drive better health outcomes through pre-emptive care while keeping healthcare costs low. It is a framework that allows payers and providers to apply existing and customized algorithms to their data to provide prediction-driven insights for proactive action. For instance, providers can use AI/ML to accurately predict the likelihood of a re-admission given the clinical parameters of a patient and their social determinants of health such as income levels, access to caretakers, education, etc. Providers may then choose to engage with a patient through multiple channels to avoid a re-admission, thus delivering value-based care. Payers, on the other hand, can apply algorithms like anomaly detection on their claims data to detect any fraud, waste, and abuse.
What role does GAVS play in this journey that a payer or provider may take?
Our People: GAVS has 40+ AI/ML experts who are industry practitioners with deep data science and analytics experience. GAVS also has a stable pipeline of AI/ML experts in training for quick deployment to any future AI/ML projects through the Long 80 Institute of Healthcare Technology (LIHT). In addition to technology experts, GAVS’ Healthcare CoE has domain experts who can bring in the perspective of the business’s requirements and pain points in order to provide our customers with a holistic value-driven solution.
Our Association with IIT-Madras: GAVS’ partnership with IIT-M gives us access to thought leadership and AI/ML experts to identify and design custom models to fit the use case for each client. Each use case goes through a rigorous brainstorming session with experts who provide other value add predictions and takeaways that can be derived from a given set of data, allowing us to exceed expectations.
Our Culture of Co-innovation: One of the key ingredients for GAVS’ success in healthcare has been the culture of co-innovation. Every partnership that began as a short-term limited scope project has grown into a large portfolio of services with GAVS as a key partner to our client’s digital transformation journey. Our people, our approach, our quality of deliverables, and the value realized by the client are testament to this engagement pattern. AI-as-a-service specifically requires healthcare organizations to choose a partner who not only can deliver a defined use case but help them identify other valuable use cases with the data they have in hand.
Our Existing Platform: ZIFTM, our existing platform provides us with several technical advantages over our peers. For example, our IP universal connector module within ZIFTM provides our clients the flexibility to share data from multiple sources and multiple formats expediting and overcoming the challenges usually involved in ingesting structured and unstructured data in healthcare across siloed systems. Additionally, AI/ML existing AI/ML algorithms within ZIFTM can be modified and applied to healthcare use cases with minimal updates once again giving us an advantage on speed to delivery and ultimately value.
GAVS believes AI/ML will be the next big disruption in healthcare, and it is being supported by various other regulatory mandates including – enabling interoperability, providing value-based care, etc., which are all focused on unlocking the power of data. Moreover, AI/ML is a lever that can be leveraged by healthcare organizations to help them move closer to the triple aim of – improving population health, better patient experience, and reduced per-capita cost.
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!
Are you able to manage your IT operations effectively? Well, managing IT operations can be more complex when you decide to scale up your IT infrastructure. The recent COVID pandemic has affected the IT ecosystem adversely. IT experts aren’t able to control the IT operations effectively as they can’t go to the workplace. All these issues led to the rise of AIOps (Artificial Intelligence for IT Operations). AIOps can help an organization to manage its IT operations and systems amidst the COVID pandemic. Read on to know about the AIOps trends reinforced by this global pandemic.
What is AIOps?
Before we move on to the latest trends in the AIOps market, you should be well aware of the AIOps definition. AIOps use the blend of AI (Artificial Intelligence) and ML (Machine Learning) to solve issues in IT operations. AIOps can enhance or replace any IT process depending upon the requirements of any particular business.
It induces automation in IT operations and makes them less tedious. Various IT processes like data analysis, event correlation, service management, and others can be automated using AIOps. AIOps platforms can cope with the increasing volume and variety of business data. AIOps help businesses to boost productivity and maintain business continuity. Even if no one is there to manage your IT operations, you can still complete key processes using AIOps.
AIOps trends fuelled by the COVID pandemic
The recent COVID pandemic led to the suspension of business activities for a long time in various countries. It was hard for business owners to maintain business continuity in these times. Companies could not provide a reliable IT infrastructure to their employees outside the office premises. AIOps use cases were discovered during this time and, businesses started using them. The COVID pandemic led to the rise of many AIOps companies and use cases. Let us see some of the latest AIOps trends reinforced by the COVID pandemic.
This pandemic forced employees to move out of their organizational workplaces. The WFH (Work from Home) culture was quickly adopted by businesses for ensuring business continuity. However, the remote IT infrastructure was not up to the mark and significantly decreased productivity. System administrators and site reliability engineers could not visit each employee’s house for fixing operational issues.
To adapt to the remote work culture, firms started using AIOps tools. AIOps can address IT issues without the need for a system administrator. Responses to common IT operational issues can be automated using AIOps. It helped businesses to decrease the downtime significantly as their IT operations are running without any human intervention.
Every business produces large chunks of data that are used to extract meaningful insights. However, with remote work culture, it gets difficult to safeguard the business data. The growing complexity of the IT infrastructure makes it difficult for cybersecurity experts to identify the source of the problem. Also, they cannot rush to the workplace for addressing data breaches due to the COVID pandemic.
An AIOps platform for cybersecurity will help you in identifying the threats and automating responses to them. AIOps solutions for cybersecurity offer strong observability into your organization’s data. The source of a cyber-attack can be easily determined via AIOps. Even if no one is there at the workplace, data breaches can still be stopped using AIOps.
Better observability lets us know about the internal states of IT systems being used. AIOps connects the IT frameworks and provides end-to-end visibility. Due to the COVID pandemic, engineers cannot visit the home of each employee to know about the internal states of IT systems. With AIOps, you can monitor the performance of IT infrastructure in real-time.
Not only for the employees, but AIOps can also provide a better user experience to customers while interacting with digital interfaces. AIOps also helps CIOs (Chief Information Officers) with digital experience monitoring. With enhanced observability, you can improve the customer experience and productivity.
AIOps & DevOps
DevOps is focused on removing the gap between the development and the operations team. However, due to the COVID pandemic, both the teams are working remotely and, it is hard to collaborate. AIOps enhances the communication between the development and operations team and allows east collaboration. Continuous monitoring of DevOps processes via AIOps will provide better results.
AIOps can automate various DevOps processes like feedback collection, monitoring, deployment, and others. AIOps vendors offer products that are capable of performing testing during the development cycle without any human intervention. AIOps products focus on strengthening the integration between different IT teams so they work together to achieve business goals.
Due to the COVID pandemic, businesses have to opt for online marketing strategies. The only way left to connect with the audience is via digital interfaces. Digital transformation not only requires adopting the latest technologies but also involves continuous monitoring. You will also have to perform data analysis to extract meaningful insights from the data produced via digital platforms.
AIOps can help you with digital transformation and can also help you make the best out of the business data. AIOps automation can help in analyzing data in real-time and extract meaningful insights. High-end data analytics via AIOps can help you in making better business decisions.
The recent COVID pandemic has harmed businesses. The market disruptions caused by this pandemic have severely affected the ROI (Return on Investment) of businesses. Business owners are looking to slash costs by downsizing their staff thus, impacting the business adversely.
With AIOps, you can automate IT operations and do not need to affect your business reach. The cost of AIOps may seem high in the beginning but is less in the long run. You will also have to spend less on AIOps training as it easy-to-use and an automated platform.
In a nutshell
The global AIOps market size will grow with a CAGR of 21.05% by 2026. It is the right time to use AIOps and steer through the challenges posed by the COVID pandemic. Use AIOps for business continuity and better uptime!
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.
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.
ZIF™ is the only AIOps platform that is powered with true machine learning algorithms with the capability to self-learn and adapt to today’s modern IT infrastructure.
ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Return on investment can be measured based on the outcomes the platform has delivered. Users get to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.
Single Pane of Action – Unified View across IT Enterprise
The biggest challenge IT Operations teams have been trying to tackle over the years is to get a bird’s eye view on what is happening across their IT landscape. The more complex the enterprise becomes the harder it becomes for the IT Operations team to understand what is happening across their enterprise. ZIF solves this issue with ease.
The capability to ingest data from any source monitoring or ITSM tool has helped IT organizations to have a real-time view of what is happening across their landscape. Enormous time can be saved by the IT engineers with ZIF’s unified view, who would otherwise be traversing between multiple monitoring tools.
ZIF can integrate with 100+ tools to ingest (static/dynamic) data in real-time via ZIF Universal Connector. This is a low code component of ZIF and dataflows within the connector can also be templatized for reuse.
Intelligence – Reduction in MTTR – Correlation of Alerts/Events
Approximately 80% of the time is lost by IT engineers in identifying the problem statement for an incident. This has been costing billions of dollars for enterprises. ZIF, with the help of Artificial Intelligence, can reduce the mean time to identify the probable root cause of the incident within seconds. The high-performance correlation engine that runs under the hood of the platform process millions of patterns that the platform has learned from the historical data and correlates the sequences that are happening in real-time and creates cases. These cases are then assigned to IT engineers with the probable root cause for them to fix the issue. This increases the productivity of the IT engineers resulting in better revenue for organizations.
Intelligence – Predictive Analytics
AIOps platforms are incomplete without the Predictive Analytics capability. ZIF has adopted unsupervised machine learning algorithms to perform predictive analytics on the utilization data that is ingested into the platform. These algorithms can learn trends and understand the symptoms of an incident by analyzing tons of data that the platform had consumed over a period. Based on the analysis, the platform generates opportunity cards that help IT engineers take proactive measures on the forecasted incident. These opportunity cards are generated a minimum of 60 minutes in advance which gives the engineers a lead time to fix an issue before it strikes the landscape.
Visibility – Auto-Discovery of IT Assets & Applications
ZIF agentless discovery is a seamless discovery component, that helps in identifying all the IP assets that are available in an enterprise. Just not discovering the assets, but the component also plots a physical topology & logical map for better consumption of the IT engineers. This gives a very detailed view of every asset in the IT landscape. The logical topology gives in-depth insights into the workload metrics that can be utilized for deep analytics.
Visibility – Cloud Monitoring
In today’s digital transformation journey, cloud is inevitable. To have a better control over the cloud orchestrated application, enterprises must depend on the monitoring tools provided by cloud providers. The lack of insights often leads to the unavailability of applications for end-users. More than monitoring, insights that help enterprises take better-informed decisions are the need of the hour.
ZIF’s cloud monitoring components can monitor any cloud instance. Data that are generated from the provider provided monitoring tools are ingested into ZIF to further analyze the data. ZIF can connect to Azure, AWS & Google Cloud to derive data-driven insights.
Optimization – Remediation – Autonomous IT Operations
ZIF does not stop by just providing insights. The platform deploys the right automation bot to remediate the incident.
ZIF has 250+ automation bots that can be deployed to fast-track the resolution process by a minimum of 90%. Faster resolutions result in increased uptime of applications and better revenue for the enterprise.
Sample ZIF bots:
- Service Restart / VM Restart
- Disk Space Clean-up
- IIS Monitoring App Pool
- Dynamic Resource Allocation
- Process Monitoring & Remediation
- DL & Security Group Management
- Windows Event Log Monitoring
- Automated phishing control based on threat score
- Service request automation like password reset, DL mapping, etc.
For more information on ZIF, please visit www.zif.ai
To survive in the current climate, most enterprises have already embarked on their digital transformation journeys. This is leading to uncertainty in the way applications and services supporting the applications are being monitored and managed. Inadequate information is leading to downtime in service availability for end-users eventually resulting in unhappy users and revenue loss.
ZIF has been architected to address the IT Ops issues of today and tomorrow.
Leveraging the power of Artificial Intelligence on telemetry data ingested in real-time, ZIF can provide insights and resolve forecasted issues – resulting in the availability of application service when end-user wants the service at the right time.
Business Value delivered to customers from ZIF
- Minimum 40% reduction in capital expenses and a minimum 50% reduction in IT operational cost
- Faster resolution by 60% (MTTR)
- Service availability of 99.99%
- ZIF bots to increase productivity by a minimum of 80%
- Increased user experience measured by metrics (UEI) User Experience Index
ICEBERG STATE IN ITOps
Many IT operations are in an ‘ICEBERG’ state even today. Do not be surprised if your organization is also one of them. Issues and incidents that surfaces to the top are the ones that are known to the team. But the unknown issues are not uncovered.
Therefore, enterprises have started to embark on artificial intelligence to help them identify and track the unknown issues within the complex IT landscape.
OBSERVABILITY USING ZIF
ZIF, architected and developed on the premise of observability, not only helps with visibility but also enables discovering deeper insights, thus freeing up more time for more strategic initiatives. This becomes critical to the overall success of Site Reliability Engineering (SRE) in enterprises.
Externalizing the internal state of systems, services, and application to the maximum, helps in complete observability.
Monitoring Vs. Observability?
Pillars of Observability – Events | Metrics | Traces
Ensure SERVICE RELIABILITY
“Reliability is defined as the probability that an application, system, or service will perform its intended function adequately for a specified period or will operate in a defined environment without failure.”
ZIF has mastered the art of predicting device, application & service failure, or performance degradation. This unique proposition from ZIF gives IT engineers the edge on service reliability of all applications, systems, or services that they are responsible for. ZIF’s auto-remediation bots can resolve predicted issues to make sure the intended function performs as and when expected by users.
Availability is measured as the percentage of time your service or system or application is available.
A small variation in availability percentage will have to be addressed on priority. A 99.999% availability allows only 5.26 minutes of downtime a year, whereas 99% availability allows downtime of 3.65 days a year.
ZIF helps IT engineers achieve the agreed-upon availability of application or system by learning the usage of the system and application from the metrics that are collected from the environment. Collecting the right metrics helps in getting the right availability. With the help of unsupervised algorithms, patterns are learned which helps in discovering when the application or system is required the most and then predicting any potential downtime. With above 95% accuracy in prediction, ZIF can achieve 99.99% availability for application and devices which allows 52.56 minutes downtime a year.
ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Users have the privilege to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.