Performance issues with essential software systems of an organization can seriously hamper service availability and ROI. Due to increased workload and complexity, application performance monitoring is a key challenge for organizations. Not all users will submit a complaint regarding low-performing applications offered by your organization. Customers may simply divert to other service providers if they repeatedly find flaws in your service applications. Find out why there is a need to make application performance monitoring more proactive.
Current Challenges with Application Performance Monitoring
Since the software systems are getting complex, application performance monitoring tools are also getting more multifaceted. Organizations have to invest in training the system administrators for using performance monitoring tools effectively.
There are real-time user monitoring tools that alert IT teams, only when a performance issue has occurred. Firms will solve the issue only after it has occurred and will experience downtime.
- The traditional application performance management solutions are unable to provide high observability into the internal states of the software systems. Poor observability makes it tough to detect performance issues within the IT infrastructure.
- Timeliness of performance alerts can have a significant impact on the costs required to fix the issue. What’s the point in knowing about a performance issue when it has crossed the critical stage? If the anomalies are not addressed on time, they could result in system failure or complete shutdown.
- When a performance issue is detected, it is hard for organizations to find out which IT team is responsible for fixing it. Collaboration between production and pre-productions teams is one of the biggest pain points for IT firms.
- Organizations are lacking predictive analytics models for predicting capacity exhaustion or future performance issues. There is a need to analyze the large amount of data produced by application performance monitoring tools.
How to make application performance monitoring more proactive?
With the challenges stated above, one can understand why there is a growing need for effective real-time user monitoring tools. AIOps (Artificial Intelligence for IT Operations) has proved to be a vital solution for eradicating the monitoring challenges faced by organizations. A few ways in which AIOps makes application performance monitoring more proactive are:
Detects patterns between software systems
No software system in an organization works individually and is related to other software systems. Similarly, performance issues are also correlated and can affect the entire IT infrastructure. Using AI for application monitoring can help you uncover interdependencies between software systems. AIOps platforms will help you in understanding the mission-critical activities for software systems.
The day-to-day data produced by essential applications are recorded and analyzed by an AIOps based analytics platform. The performance data from various sources is matched by an AIOps platform to uncover patterns/clusters. If you can identify patterns between performance issues, you can detect various anomalies even before they occur.
Analyzes customer data
Why depend on customers to report an issue with your service applications? AIOps-based real-time user monitoring tools can collect customer and transactions data. AIOps-based platforms will monitor the user experience of customers and will report a performance issue even before the customer finds about it. When an organization knows about the performance issues with service applications in advance, it can resolve them without hampering service availability.
Forecasting performance issues
AIOps uses predictive analytics models to forecast performance issues within the IT infrastructure. AIOps platforms analyze the historical and current performance data of applications to understand behavioral changes over time. Predictive analytics using AI applications can also help you gain a competitive advantage as you will ensure the high uptime of your software systems.
For example, AIOps can identify if there is a change in how the customers are interacting with your service applications. Predictive analytics business forecasting will help you in solving an IT incident before it impacts the performance.
Studies have shown that AIOps can help in reducing the cost of resolving performance issues by 30% to 40%. Besides cost optimizations, AI data analytics monitoring tools can significantly reduce the MTTD (Mean Time to Detect). When you can find the prevailing performance issue in less time, you can work proactively to resolve them.
AIOps platforms are an AI automated root cause analysis solution that quickly finds the source of a performance issue. Traditional application performance monitoring tools follow a siloed approach and provide limited information to the IT teams. Contrary to that, AIOps analyze diverse information streams to produce actionable insights. It will also help in managing your service availability and reliability.
In a nutshell
The worldwide AIOps industry is predicted to progress with a compound annual growth rate of 34% by 2025. Many organizations are already using AIOps based analytics platforms for monitoring the performance of business applications proactively. AIOps platforms will provide you with high-end analytics that can help in managing applications performances proactively.
The rise of AIOps based analytics platforms has helped organizations induce automation in the business processes. AI data analytics monitoring tools rely heavily on huge chunks of data produced by machines to derive insights. Amazon is one of the leading firms in providing components of IT infrastructure to organizations across the world. It has offered various AWS tools that are specifically designed for implementing an AIOps strategy. Find out why AWS is the right tool for effectively implementing AIOps.
Gain visibility into the Amazon S3
Organizations use Amazon S3 for object storage and require high observability to detect data types. Amazon S3 can store numerous types of objects and, it gets difficult to detect sensitive data types from the large pool of data. To tackle this challenge, AWS launched Amazon Macie to discover sensitive data stored in S3. Let’s delve deeper into how Amazon Macie enhances observability into S3:
- Just like AIOps, Amazon Macie uses pattern matching and AI/ML algorithms to detect sensitive data types.
- It also uses PII (Personally Identifiable Information) like name, address, and others to automatically detect sensitive data types.
- Besides discovering standard data types, Amazon Macie can also discover customized data types. You can use regular expressions to discover sensitive data types easily with Amazon Macie.
- The best AIOps tools and products can be used together with Amazon Macie to increase visibility into the security and privacy of sensitive data. AIOps tools are highly compatible with Amazon Macie to aid organizations in detecting sensitive data types.
Curbs the need for manual scaling
Many organizations use Amazon EC2 for boosting their cloud computing infrastructure. The EC2 fleet size is to be minimized and expanded according to utilization. AIOps-based real-time user monitoring tools can also compute the exhaustive capacity of EC2 instances. However, Amazon offers predictive scaling that completely exempts CXOs from manual intervention. If the workload on your EC2 instances experiences sudden spikes, predictive scaling is the perfect solution for you.
Predictive scaling is a type of AI DevOps platform management service that predicts the spike in the workload of EC2 instances. Based on weekly/daily traffic on EC2 instances, predictive scaling can determine future traffic spikes. You can use AIOps-based predictive analytics models to determine the future performance of software systems. However, with EC2 predictive scaling, you can know the future traffic patterns which, are the main reasons for scaling the computing capacity of your software systems.
Anomaly detection via Amazon CloudWatch
Amazon CloudWatch is among the best AWS cloud monitoring tools. CloudWatch was launched in 2009 but didn’t have anomaly detection as a built-in specification. With the rise of AIOps-based application performance monitoring systems, Amazon offered an anomaly detection feature for CloudWatch in 2019. Amazon CloudWatch can help with anomaly detection in the following ways:
- CloudWatch’s anomaly detection feature uses ML algorithms. It has its roots in Amazons’ many internal/statistical models signifying its vast capabilities to detect an anomaly.
- Various metrics are used to measure the application performance monitoring. CloudWatch analyzes the historical data related to performance metrics and finds patterns between the anomalies.
- Based on the historical data, CloudWatch forms a prediction model that depicts future anomalies. IT professionals that are involved in enhancing service availability will be benefited from Amazon CloudWatch. CloudWatch will provide you with actionable insights which, is also the main function of AIOps-based platforms.
- You can set CloudWatch alarms that automatically go off once an anomaly is detected. It is among the widely used AI data analytics monitoring tools that collect performance data in form of metrics, logs, and events. You can automate actions for resolving anomalies by using CloudWatch to implement AIOps.
Root cause analysis
Are you unable to find the root cause of security breaches in your IT infrastructure? Amazon Detective is an AI automated root cause analysis solution that helps during security breaches or suspicious behavior. It uses statistical analysis via AI/ML algorithms to find the root cause of a security issue. Unable to find the root cause of a security issue will hamper your service availability. Implementing AIOps security with Amazon Detective will help you in collecting log data from various software systems. The log data collected by Amazon Detective is then organized into a graph model.
The data in the graph model organized by Amazon Detective is continuously updated and, it keeps log data up to a year. It makes tracing security issues and their root cause easier for system administrators. By looking at the aggregated log data, you can easily find the patterns between security issues. Combining log data from various data sources manually is a tricky job as security investigations tend to get lengthy. An AI automated root cause analysis solution can help in conducting faster security investigations.
Intelligent threat detection
Security threats are getting more complex and, businesses are adopting intelligent threat detection to encounter them. Thanks to AIOps, cybersecurity and compliance services can be conducted more effectively. Amazon GuardDuty is another widely used AWS tool for intelligent threat detection. How you can use Amazon GuardDuty to implement AIOps for your organization are as follows:
- In the backdrop of Amazon GuardDuty are sophisticated AI/ML algorithms that continuously monitor AWS resources. Besides monitoring the data stored in Amazon S3, GuardDuty also monitors AWS accounts and workloads related to your S3 instances.
- GuardDuty is among the best AIOps solutions in USA that does not require any external software/hardware to deploy. You can activate Amazon GuardDuty in simple steps via the AWS Management Console.
- GuardDuty will help you in identifying the current as well as the future threats within the IT infrastructure. Not only do you automate security & compliance processes but also save funds from being unnecessarily spent.
In a nutshell
More than a million organizations worldwide are already using AWS resources. The AIOps industry is relatively newer but has an impressive compound annual growth rate of 30%. AWS offers AI tools in IT operations management that can implement the right AIOps strategy for your organization. Start using AWS to effectively implement AIOps for your business!
Organizations across the world saw their business processes become severely constrained due to the COVID-19 pandemic. Since physical workplaces were shut down, businesses found it hard to maintain business continuity. Organizations quickly started adopting the WFH (Work from Home) culture to maintain business continuity. This culture brought its challenges and organizations had to cope with them. One such challenge was to manage security & compliance remotely.
Challenges of remote working
The coronavirus outbreak brought unprecedented challenges. While most companies struggled with maintaining team coordination, communication, and performance standards, technical challenge further prevented business leaders from managing productivity. Most organizations did not have a dedicated IT infrastructure to handle remote IT operations. Hyper-automation and a dedicated remote working IT infrastructure became the most sought-after transformation and companies were quick to embrace newer technologies. AI tools in IT operations management were the best solution for managing remote working.
How AIOps helps remote working
AIOps platforms use AI/ML algorithms to support IT operations and automate them. AIOps focuses on decreasing the need for human labor. The COVID-19 pandemic increased the demand for AIOps based analytics platforms. These platforms are seamlessly helping organizations adapt to the WFH culture by offering distinct features:
1. Remote monitoring
The biggest challenge with remote working was to monitor the performance of software systems responsible for IT operations. CIO/CTOs would not be able to access the workplace IT infrastructure to monitor the performance of software systems. System monitoring can help identify the incidents within the IT infrastructure. AIOps platforms can implement rigorous monitoring of software systems without the need for human intervention.
AIOps can collect data from multiple and remote sources. The temporal data will then be analyzed to find any outliers or anomalies. You can identify incidents within your remote IT infrastructure and can resolve them to maintain business continuity. Real-time user monitoring tools can help in solving incidents faster.
2. Incident resolution
With AIOps, you can find the root cause of an incident easily. Even if the devices connected to your IT infrastructure are at remote locations, AIOps will still identify the incidents. An AIOps based analytics platform utilizes temporal data to identify incidents.
Upon the discovery of an incident, an AIOps platform will inform you about the IT team responsible for resolving it and provide actionable insights to resolve an incident faster.
If an event occurs twice within the remote IT infrastructure, AIOps platforms can remember it. Remembering an incident means they also recall the steps needed to solve it.
Hyper automation for ITOps is automating all the processes except for those that can only be managed manually. You cannot invest in installing high-cost servers at each employee’s home to achieve better results. Firms are looking to automate IT operations to maintain business continuity in such times. AIOps platforms can help in automating several IT operations with their intelligent AI and ML algorithms. The costs for automating numerous IT processes may seem high in the beginning but will be beneficial in the long run.
Besides monitoring the user experience, you also must monitor the performance of systems used by the employees to perform essential business operations. AIOps platforms help in enhancing the observability in user experience and the internal states of the software systems. Since your employees are working remotely, you must make sure that their software systems provide high performance. Low-performing software systems can result in lower productivity and higher downtime.
If an issue arises with any essential software system, AIOps can identify it even before an employee reports it. Enhanced observability with AIOps helps in digital experience monitoring. Firms use various digital platforms to connect with their customers. If an issue occurs with the user experience on digital platforms, AIOps can find it for you in advance.
5. Enhanced collaboration
Communication has been a key challenge amidst the COVID-19 pandemic. DevOps teams are not able to remove the communication gap between the operations and development teams. AIOps platforms not only reduce the communication gap between IT teams but also allows east collaboration. Processes like feedback collection can be automated using AIOps. You can collect feedback from your employees and customers at regular intervals to understand issues with the IT infrastructure.
6. Security & Compliance
Security & compliance is an indispensable business process for organizations. Firms are producing large volumes of sensitive data that need to be protected. Cybersecurity experts cannot visit the workplace to access security platforms. Remote work culture lets employees work from multiple devices and thus, safeguarding the business data gets even more difficult. If any data breach occurs, one cannot rush to the workplace for accessing the resources needed to solve it.
AIOps based analytics platforms identify the potential risks within the IT infrastructure in advance. You can automate the basic steps to follow if a data breach occurs with a reliable AIOps platform.
7. VDI (Virtual Desktop Infrastructure)
Virtual desktop infrastructure solutions are in demand due to the COVID pandemic. VDI enables employees to work remotely and can access IT resources. Organizations can deploy computing capacity, enterprise applications, and other IT components on the devices of employees via VDI.
If there is a VDI desktop virtualization software system, its performance must be monitored. Data about the performance of virtual desktops has also to be analyzed. AIOps can help in managing your VDI and making sure the employees are provided with the computing capacity that they need. AIOps can monitor the user experience of virtual desktops to identify any issues. You can also eliminate any potential risks within the VDI with a reliable AIOps platform.
45% of businesses have already started using AIOps to ensure business continuity and remote monitoring of software systems. Even if firms cannot access workplace resources, they are able to maintain business continuity with AIOps.
Has your organization adopted AIOps for effective IT operations management? How do you measure the impact of AIOps transformation on your business?
Measuring the impact of AIOps depends on Key Performance Indicators (KPIs) – a measurable value that denotes the performance measurement.
These KPIs are critical in measuring the impact of AIOps on your business:
1. Ticket-to-incident Ratio
Users and system administrators generate tickets for an issue. When an issue/incident is being reported, it is often noted that numerous tickets are raised for a single issue. It may not look daunting when the number of incidents is low. However, for businesses, thousands of tickets are raised in a single day. If you do not eliminate redundant tickets, each ticket will be matched to an incident, with the ticket–to–incident ratio being 1:1.
AI tools in IT operations management can help you efficiently identify and resolve tickets that report the same issue. You can save IT teams, from working on redundant incidents by matching numerous tickets to one incident. Change in ticket-to-incident ratio for your IT department can be used to measure the impact of AIOps on your business.
2. Service Availability
Service availability is measured in terms of the uptime of software systems. Uptime is determined via the number of outage minutes in a fixed time frame. Software systems for a business are responsible for operating important business processes. An organization cannot provide services to its users if employees cannot get access to reliable software.
AIOps can help solve the incidents in your IT infrastructure without any manual efforts. Simple and repetitive incidents can be solved in real-time with AIOps. This ensures that your software systems will be subjected to fewer outages. The higher the uptime of your software systems, the higher will be the service availability. An increase in service availability will denote the positive impact of AIOps on a business.
3. MTTD (Mean Time to Detect)
MTTD is the time taken to identify an incident within your IT framework. How will you solve incidents in real-time if you cannot detect the source of the incident? A business uses many software systems and, sometimes, it is difficult to identify where the problem originated. For example, a user reports to you that the product website is down. Even though you analyzed all the software systems, you still cannot find the bug responsible for website failure.
AIOps analyses patterns and relationships between various incidents. It also makes the best use of historical data to find the origin of the incident. With AIOps, you can significantly decrease MTTD for your business. Some sectors like healthcare used AIOps so that an incident among the critical software systems can be identified quickly.
4. MTTA (Mean Time to Acknowledge)
MTTA is the time taken to acknowledge the team/person responsible for solving a particular incident. If your product website is down, how will it be solved by a database expert? You will need a web developer for solving the issues on your product website. Once an issue within the IT infrastructure is identified, it must be transferred to the responsible person/team.
AIOps platforms use intelligent algorithms that also inform which team/person is responsible for solving an incident. A decrease in MTTA for your business will denote the positive impact of AIOps. AIOps platforms offer real-time user monitoring tools that can provide meaningful insights for IT operations.
5. MTTR (Mean Time to Repair)
When an essential business application or website is down, it also affects your revenue. For example, you will not generate any sales if your e-commerce application is down. MTTR is the time required to solve an issue/incident within the IT infrastructure. MTTR starts from the time an issue is discovered till the systems get back fully online.
AIOps identifies the root cause of an incident within the IT infrastructure. Also, due to decreased MTTA caused by AIOps, IT teams will not waste time deciding who will solve the issue. If the incident has occurred in the past, AIOps platforms will remember it. Based on the historical data, an AIOps platform will provide you with actionable insights to solve the issue. A decrease in MTTR for your business will denote the positive impact of AIOps.
6. MTBF (Mean Time Between Failures)
The average time between system failures/outages is called MTBF. For example, consider a software system that has operated for 100 hours and in between, it experienced downtime two times. The MTBF of the software system will be 50 hours. AIOps helps you solve an incident in real-time and thus, not letting the systems go offline. An increase in MTBF for your business will denote the positive impact of AIOps.
7. Automated vs Manual Resolution
With AIOps, you don’t need manual interference every time for solving IT incidents. You can set pre-decided actions against redundant incidents with the help of AIOps. AIOps offer automation tools for service desks that can eliminate the need for human labor. If the number of times you have solved an incident manually is less than automated resolutions, you can say AIOps is working just fine.
The global AIOps market will be worth more than USD 20 billion by the end of 2026. You can reduce the time taken to identify, acknowledge, and solve IT incidents with AIOps and leverage KPIs to measure the impact of AIOps on your business.
In today’s competitive and software-driven era, businesses must make better decisions to stay ahead in the market. The IT infrastructure of a business is responsible for essential business processes. Businesses invest more in the management and security of their IT framework so that their essential operations do not stop. However, with the growing business data and customer needs, managing IT infrastructure has been tougher than ever.
AI tools in IT operations management offer enterprises a way to achieve better results.
The need for a responsible AI framework
The traditional IT framework is not able to match the huge volumes of business data produced every day. Business data needs to be analyzed and protected from intruders. Traditional software systems cannot upscale themselves automatically with the growing need. You will have to restructure your IT framework frequently as per growing demands.
A framework is a foundation on which business applications and other components of IT infrastructure are built. An AI framework is scalable and requires less human intervention to operate. IT automation with AI tools will also enhance the productivity of an organization.
Things to know before building a robust AI framework
1. Building a dependable AI framework isn’t easy
AI adoption can be complicated as its impact is tricky to measure. An organization can go for hyper-automation, but that will involve high investment. Implementing a complete AI framework for each of your business processes will help you achieve hyper-automation. It is important to note that firms that achieved hyper-automation also started small.
Analyze which IT operations are most vulnerable and demand a scalable solution. Once you have identified them, apply AI only to those IT operations. There is no point in exhausting your funds at once by going for a full-fledged AI solution. Once AI is successful for initial test cases, you can start building your AI framework piece by piece.
2. Leverage the power of AIOps platforms
AIOps (Artificial Intelligence for IT Operations) is a scalable solution to automate and enhance the productivity of your IT operations. AIOps platforms are also helping organizations monitor remote work extensively. The recent COVID-19 pandemic has forced organizations to look for automated IT operations. The main benefits of using AIOps for your business are as follows:
- AIOps managed infrastructure services help reduce the need for human intervention.
- With a reliable AIOps based analytics platform, you can identify the incidents within the IT infrastructure easily.
- AIOps platforms use AI/ML algorithms to provide actionable insights for solving an incident within the IT framework.
- You can use AI for application monitoring amidst the remote work culture. AIOps platforms offer enhanced observability into software systems to monitor their performance. Virtual desktop infrastructure solutions powered by AIOps are also available in the market to adapt to the WFH (Work from Home) culture.
- With AIOps, you can identify vulnerabilities associated with the software systems like exhaustive capacity, outage prediction, and much more.
- AIOps platforms for your IT framework will help the IT teams collaborate quickly. The disorientation among the IT teams will be drastically reduced by adopting AIOps.
- AIOps platforms can offer real-time information about cyber-attacks to boost cybersecurity.
You can identify the skill gaps within your organization and implement AIOps to fill them. System administrators, CIOs, or CTOs always aim to monitor the performance of software systems end-to-end. With AIOps, not only can you monitor the software systems rigorously, but also resolve incidents faster within the IT framework.
3. Decide your performance metrics
Benchmarking is critical for identifying the impact of AI on a business. While designing an AI framework for an organization, IT leaders should decide on performance metrics to be used. Some of the best performance metrics for measuring the impact of AI on an organization are as follows:
- Service availability: The availability of your applications and systems to provide essential services to customers translates into business reliability. With AI adoption, you can significantly enhance your service availability.
- MTTD: MTTD (Mean Time to Detect) is the average time taken to identify the root cause of an incident within the IT framework. With AI adoption, you can decrease the MTTD.
- MTTR: MTTR (Mean Time to Resolve) is the time taken to fix an issue within the IT framework. A reduction in MTTR will denote the positive impact of AI on your organization.
- MTBF: MTBF (Mean Time Between Failures) is the average time between IT outages. For example, if the software systems of an organization fail three times after operating 300 hours, we can say the MTBF is 100 hours.
Some other metrics for measuring the performance of AI-powered systems are service reliability, MTTA, the ticket to incident ratio, automated versus manual workload, and many others. Deciding the metrics for measuring the performance of AI-powered systems should be done while implementing an AI-led IT infrastructure.
4. Look for a responsive and reputed AI firm
There is a skill gap in the industry when it comes to AI experts. Organizations find it hard to ensure ethical use of intelligent AI algorithms and high-end analytics. Organizations are already using traditional monitoring systems and are not sure about the algorithmic fairness of AI platforms.
You should use AI monitoring tools provided by reliable AI firms. Pre-made AI solutions are designed according to industry standards. With premade AI solutions, you can decide the extent of automation to be induced in the IT framework.
In a nutshell
More than 50% of businesses have reported that adopting AI has boosted their productivity. Implementing a responsible AI framework for your company will help you cut costs in the long run. Start exploring AI-based platforms for your company now!
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!
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!
The cloud is now a primary place for SMEs and other large enterprises, and Microsoft’s Azure is considered one of the preferred IaaS and PaaS services for most business organizations.
As Artificial Intelligence and Machine Learning are changing the digital way of life, AIOps is set to uplift cloud services and make operations easy for the IT industry. It provides users with a broader range of benefits, including better customer experience, service quality assurance, and productivity boost.
Why Does Your Organization Need AIOps With Microsoft Azure Ecosystem
As cloud usage is in high demand, businesses are facing problems in managing their cloud infrastructure. AIOps for Azure provides better efficiency with the help of AI-driven software, ensuring smoother operations.
By executing AI operations and ML on Microsoft Azure, organizations can be benefited in many ways. Some of these are:
- Efficient and Cost-Effective Infrastructure
Microsoft Azure helps lower the overall cost of a business when enabled with AIOps and MLOps. AI and ML help make Azure cloud a better choice for Machine Learning Operations and Artificial Intelligence Operations.
- Edge Computing
Edge processing aims to bring data resources closer to the users, thus improving the overall performance of the cloud infrastructure. It also helps reduce cost and increase processing capacity simultaneously.
- Pre-Trained Machine Learning Models
The Microsoft Azure Platform offers pre-trained models. These can be used for a custom model for tailor-made processing of the company’s workloads. Many ML programs can be used as models through MicrosoftML for Python and MicrosoftML for R for various functions.
Manage Your Azure Infrastructure Easily With AIOps
Microsoft Azure is a reliable cloud service that manages data efficiently. As the cloud is always increasing and becomes complex as each day passes, it needs more developers and engineers to make it stable. It can become quite easy to remain at par with the constantly evolving cloud if there were a solution to make data-based decisions automatically.
Not only will this save a lot of time for the resources of your organization, but also make the process more efficient. AIOps and machine learning help streamline the process and assist engineers in taking actions based on the insights from the existing data.
AIOps is based on self-monitoring and requires no human intervention. Automation of services ensures improved service quality, reliability, availability, and performance.Azure cloud professionals are no longer required to investigate the repeated process and manually operate the infrastructure. Instead, they use AI and ML engineering. AI operations can work independently, and human resources can utilize their time to focus on solving bigger problems and building new functions.
Design Your Own Growth Path by Systemizing Your Operations With AIOps
The AIOps framework can contribute in several ways. The major elements are explained below.
- Extensive and Diversified IT Data: AIOps is predicted to bring together data from IT operations management and IT service management. Bringing data from different sources helps accelerate root cause identification of a problem and enables automation simultaneously.
- Big Data Platform: The center of an AIOps platform is big data. As data is collected from different sources, it is required to be compiled together to support next-level analytics. AIOps aggregates big data and makes it accessible to be used in real-time.
- Machine Learning: Analysing big data is not possible by humans alone. ML automates and analyzes new and diversified data with a speed that is unachievable without the AIOps framework.
- Observation: It is the emerging of the traditional ITO domain and other non-ITOM data to enable new models and correlations. The combination of AIOps with real-time processing makes root cause identification easier.
- Engagement: The traditional domain offers bi-directional communication to support data analysis and, thus, auto-creates documentation for audit while maintaining compliance. AIOps help in cognitive classification with routing and intelligence along with user touchpoints.
- Act: This is the final stop for the AIOps strategy. It provides the codification of human knowledge into automation. It helps automate analysis, workflow, and documentation for further actions.
What Does the Future Have in Store for IT Operations?
Artificial Intelligence for IT operations is bringing a continuous change in the cloud business. In no time, adopting the AIOps way will become a necessity.
- Accelerate Digital Transformation: Sooner than later, businesses will be able to offer data-driven experiences with the help of AIOps. It won’t be a hassle to migrate systems after systems, as most of the monotonous work will be handled by automated systems. This way, businesses can easily transform digitally to remain relevant
- Solutions to Various Challenges: Often, when humans spend time performing basic calculations, a lot of time and energy is wasted. Moreover, there is always a chance of human error. Empowering developers with actionable insights, AIOps will make solving problems hassle-free, replacing many traditional monitoring tools
- Finding Issues Automatically: A faster and more efficient way to improve customer satisfaction involves ensuring that there are no problems with your service or product. However, this can be challenging. With AIOps solutions, identifying issues and mitigating them will be a cakewalk. It will play an essential role in troubleshooting workloads and understanding and predicting customer needs in the current competitive environment, eliminating the need for having a dedicated team of resources to solve simple issues.
How Does AIOps Transform a Business?
- Digitization of Routine Practices
The AIOps architecture helps digitize routine practices, like user requests, while processing and fulfilling them automatically. It can even evaluate whether an alert requires action and if all the supporting data is under normal parameters.
- Recognizing Serious Issues Faster and More Accurately
There are chances of human error while looking out for threats. This may lead to an unusual download being ignored. AIOps tools tackle can solve this problem easily. It can run an antimalware function through the system, automatically and when required.
- AIOps Streamline the Interactions Between Data Center Groups and Various Teams
AIOPs shares all the relevant data with each IT group and provides the operations team with what they require. Manually meeting and sending data is no more required, as AIOps monitors data for each team to streamline the interactions between all groups.
With the help of Microsoft Azure, the value of companies associated with this ecosystem is scaling in an upward direction. To conclude, it can be rightly said that AIOps is the infusion of AI into cloud technology. When properly implemented, AIOps can help reduce time and attention on the IT staff of an organization. AIOps open-source tools allow Azure cloud professionals to observe multiple systems and resources. With better ML capabilities, it can enable software to find the root cause of a problem and accelerate troubleshooting by providing the right remedies for all unusual issues of an IT organization running on Microsoft Azure.
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.