ITOps Times AI Ops Buyer's Guide

AIOps

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A GUIDE FOR BUYERS

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AIOps

a guide for buyers

Contents

AIOps: Beyond the buzz… it’s changing how IT operations are run page 4

Hybrid AI in the Future of Work page 12

Survey: AIOps seen as delivering value page 14

AIOps Exchange lays the groundwork for the future of IT operation page 15

How does your company help organizations with their AIOps initiatives? page 10

A guide to AIOps tools page 16

September 2019

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AIOps

Beyond the buzz… it’s changing how IT operations are run

By Jenna Sargent

The tech industry has a tendency to latch onto whatever the latest technology is and hold on tight. it happened with cloud. it happened with ai. and now it’s happening to aiops. according to Will Cappelli, CTo at Moogsoft, aiops has become a sort of blanket term. He recounts that at almost every conference he’s been to this summer, and even late spring, he’d go to the show floor and every vendor was claiming to be an aiops provider. “Precisely because of its impact, it’s becoming a term that is being used to mean almost anything by almost everybody,” he said.

4 September 2019

because of this spike in the number of companies self-describing as aiops vendors, when they might not be truly providing aiops solutions, it’s important to understand the technical definition. according to Cappelli, the high-level definition of aiops is that it is the “application of ai technologies or ai-style algorithms to iT operations use cases.” by iT operations use cases, he is referring to the entire range of issues that might be covered by iT operations management.

James Moore, senior product manager at ibM, describes aiops as the “latest technology that helps re-

ally accelerate and manage digital transformation efforts that are affecting operations management. and moving from traditional operations management to more of an agile, proactive and automated way of approaching the overall iT operations methodologies.” according to Moore, aiops involves analytics of incoming information and the ability to build trends off that data. “extending that to postmortem problem management activities, such as understanding where the intrinsic problems are happening and can automatically be flagged to be addressed at the

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term was first coined. initially, aiops was considered by some to just be the predictive or automated aspects of iT, he said. “What we’re seeing is that it’s becoming an evolution from years of best practices in years of iT operations management. Leveraging analytics and machine learning at each step of the way, and building in layers of automation at each step of the way. so it’s becoming more of a holistic approach that we’re seeing, really elevating how operations is managed overall. Not just one way of taking data and doing analysis on top of that and trying to do some predictive assessments. so that’s definitely still part of it. That’s not all there is. That’s one thing that we’re seeing change,” Moore said.

Overcoming the concern around AIOps

source so that you’re moving from just reactive mode to more proactive ways of taking care of problems where they sit at their source.” aiops has come a long way since its inception. and Cappelli argues that the idea of aiops is not a new concept. ai and iT operations management software have been closely linked since the 1980s, he explained. but the market is certainly growing. according to gartner, the use of aiops tools will rise from 5 percent in 2018 to 30 percent in 2023. and Moore believes that what aiops is has changed since the

for a while, there seemed to be concern around aiops among those in the industry. some worried that aiops might replace iT workers, while others worried about the accuracy of systems, data quality, skills gaps, and transparency of machine learning algorithms. according to Moore, there is a general acceptance of aiops when it comes to mundane everyday tasks. but when you get to the more thorough automation situations, that is where aiops could potentially have some sort of adverse impact and that’s where the concern lies. one way to combat these concerns is through “achievable automation” and explainable ai, Moore explained. achievable automation means that aiops should be adopted in steps. Moore recommends iT teams start by automating the man-

According to Will Cappelli, CTO at Moogsoft, there are five different types of algorithms that could be applied to IT operations:

l l l l l

Data selection: selecting data sets from the vast volume of data available Pattern discovery: finding patterns in

data sets

Inference: inferring meaningful implications of those patterns

Communication: Communicating the results of those inferences

Automated remediation: driving the automation of those conclusions and the required remediation

ual tasks that they are currently dependent on. “you can start instilling layers of automation there. and it becomes more of an escalator versus a launching rocket or high speed elevator, i guess you could say. and what that lets you do is achieve automation in steps,” Moore said.

Moore recommends that at first you should have ai automate the manual tasks, while still leaving to humans the decision-making and execution of actions that could impact other parts of the infrastructure. once an iT team has done this successfully, they could then start to fully automate their iT systems.

The length of time it takes for an organization to become fully automated will vary. some teams will go through that evolution fairly quickly, while it may take others longer to get to the point of full automation, Moore explained. secondly, ai systems must be able to explain why they generated the results they did in order to be trustworthy. continued on page 6 >

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AIOps is changing how IT organizations are run < continued from page 5

one of the challenges that aiops is attempting to solve, according to Cappelli, is that traditional iT operations professionals and developers, no matter their experience, struggle with seeing through the complexity of data to find patterns. He has seen two different responses to this challenge among aiops vendors. one solution is to provide a set of tools that data scientists can work with to help them see the patterns. another approach is to automate that pattern discovery. “so effectively not just provide tools that make it easier to discover patterns, but to automate the pattern discovery itself so that you don’t need to bring in a team of data scientists to see these complex patterns in the data,” Cappelli said.

No matter which route companies decide to take, it’s important to understand that the patterns iT organizations are dealing with today are much more complex than ever before. No matter which approach you go with, there is still a level of understanding and a level of thinking of iT systems and behaviors that is needed, Cappelli explained.

Challenges of AIOps

one challenge that organizations face is that when implementing aiops, they have to get used to the idea that their iT systems no longer function in isolation, Cappelli explained. This can make implementing aiops difficult for very siloed organizations. but even without aiops in the picture, it is important for today’s iT organizations to break down those

6 September 2019

The three most common use cases for AIOps

The use cases for aiops can be condensed into three top use cases, Will Cappelli, CTo at Moogsoft, explained. one use case is for cutting through the data that’s generated by iT systems. according to Cappelli, a lot of that data tends to be redundant and therefore highly noisy. it’s important to be able to determine which events are most likely to impact business processes. “every day an iT operations professional is literally faced with millions of events and he or she without automated assistance is not going to be able to figure out which are the events that are important, which are the ones that are not,” he said. another common use case for aiops is for ensuring that the correct team gets assembled when problems emerge. aiops needs to be able to support the collaboration that is needed to deal with issues, he said. and finally, the third common use cases is a mix of the first two. The third use case is finding out the relationship between events. “you’ve determined that 10 events are very critical to the successful execution of your digital business process. and then you need to figure out, of those 10 events, what is the causal relationship among them? Which of those events do you need to intervene upon in order to ensure that you’re actually fix the problem,” said Cappelli. siloes. iT services have become more interconnected and affect one another, while the organizations running those services have become more siloed. “aiops really kind of forces that kind of breakdown of those silos and forces iT operations teams at all levels to treat the problems holistically, treat the problems in a way that’s oriented toward digital business. i think that forcing silo breakdown is one of the more challenging things that aiops technology brings to the table,” said Cappelli. similarly, differences in knowledge, approaches to processes, and lack of flexibility in how critical ap-

plications have been deployed will also present a roadblock, explained ibM’s Moore. He recommends organizations look for a solution that is “flexible enough to be able to fit into those different types of situations, but also augment what you’ve already got so you don’t have to have a long lead time in order to take advantage of aiops.”

What to look for in aiops tooling When looking for an aiops tool, it’s important that companies are able to sort through some of the marketing terms.

The most important thing to look for in an aiops solution is that it can continued on page 8 >

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AIOps is changing how IT organizations are run < continued from page 6

deliver meaningful results. it’s important that a tool be able to go beyond basic pattern discovery and actually be able to tell you which patterns are meaningful, in terms of their impact on the business. “you don’t just want to know that a particular CPu exhibits a certain changing behavior and consumption over the period of the week. you want to know that on

“AIOps helps really accelerate and manage digital transformation efforts that are affecting operations management.”

—James Moore

Thursday, if it hits a certain level of consumption, that is going to cause a degradation in the response time that your customers are looking for in your website,” said Cappelli. another important thing to consider is whether the tool can adjust to changing environments in an automated way. according to Cappelli, many aiops tools rely on a rulebased system where the rules are defined before deployment. but a 8 September 2019

rule that is valid one week may change if the environment changes. “one of the things that we think you should be very wary of are systems like rule-based systems that tend to be very very brittle and require continuous maintenance in order to function properly,” he said.

The final thing that should be considered is whether the solution can provide an explanation on how the system came to the conclusion it came to. Neural network algorithms are one thing to be wary of because they can rarely ever explain how they generated their results, said Cappelli. “That’s not to say you should never use neural networks, but just be aware that there will be very frequently issues in your ability to explain the way in which the system has come to the conclusion that it has come to,” said Cappelli.

AI will soon expand to all corners of IT operations

ai is already starting to be applied to other areas of iT operations, such as configuration and change management and problem and incident management. Cappelli expects that this extension into other areas will become even more pronounced in the years to come.

He also predicts that aiops will start creeping out into the world of the internet of Things. “you’ll see very much the same products or minor variations of products that have been originally designed to bring ai to the management of enterprise iT infrastructures and application portfolios being extended to smart buildings, smart factory floors, and then out into the world of

the internet of Things as well,” said Cappelli. in addition, it will spread to security products as well, likely in the next 18 months. Those five algorithms in use in aiops (data selection, pattern discovery, inference, communication, and automated remediation) will begin to be applied to security use cases as well, Cappelli explained.

Moore has also seen the emergence of more proactive aiops methodologies. Companies can now use aiops to predict the likelihood of problems emerging or existing problems getting worse. “[for example, maybe] certain builds culminate in a common problem every Tuesday evening. so if that can be easily detected and predicted, then the way that you prepare people to react to those, you’re essentially planning for the failure. Not hoping it won’t happen, but planning that it will. so you’re preparing the teams to react if and when that does occur, in a similar way. and you’re working, you have the tools and information to work towards solving that problem at the source so that it doesn’t happen,” said Moore. finally, aiops is now being applied to emerging problems, rather than just the age-old problems it was created to solve, Moore explained. Three key pressing issues to be addressed by aiops include the volume of data being generated, higher levels of user experience, and the adoption of new technologies. it’s clear that while it seems like aiops has only started gaining rapid traction recently, it is showing no signs of slowing down. as it becomes more prevalent in iT organizations, it will truly reshape the way that companies do iT operations. n

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Continuous Service Assurance with Moogsoft AIOps Moogsoft is a pioneer & leading provider of patented AIOps solutions analyzing billions of events daily across the most complex IT environments. We help IT Operations & DevOps teams at the worldâ&#x20AC;&#x2122;s top enterprises work faster & smarter.

Learn More

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How does your company help organizations with their AIOps

By Jenna Sargent

James Moore, senior product manager at IBM

ibM’s cutting-edge aiops capability helps enterprises at any stage on their digital transformation journey to reduce costs dramatically and ensure business continuity with highly intuitive, proactive and flexible ai operations features, delivered via ibM Netcool operations insight (Noi).

With Noi’s intuitive aiops solution, intelligent analysis of your managed infrastructure operations is performed for you automatically, saving what would otherwise require tedious hours of investigation to uncover patterns and trends that are not obvious. Context is provided in a clear visual display that bridges both real-time and historical data, allowing you to scroll back in time to learn from your operations data. Topology and dependencies are analyzed and a recipe for resolution or action is provided, enabling you to respond faster to improve your operations and reduce ongoing cost. ibM Noi’s aiops also enables you to move from reactive to proactive operations by learning from your infrastructure over time and taking actions to avoid problems in advance. your operations cost savings continue to improve, as ibM’s machine learning algorithms learn from your event and performance data to help automate actions and runbooks — freeing your staff to focus on more strategic initiatives.

10 September 2019

What IBM Netcool Operations Insight dashboard issues that previously took hours to resolve manually can now be reduced to minutes — and, over time — seconds, as you evolve with greater automation.

Noi’s aiops flexible operations can adapt to any environment with simple out-of-the box integration and evolves just as easily. so, whether your infrastructure and applications are on-premises, cloud, hybrid, or multicloud, ibM has you covered. With comprehensive support for operations management, advanced analytics and performance management, ibM gives you the flexibility to adapt to your digital transformation journey, at your own pace.

Learn how ibM Netcool operations insight can support your high performance iTops and devops teams with the latest in aiops tools and capabilities to be more intuitive,

proactive and flexible. aiops is part of the ibM hybrid multicloud management strategy. find out how ibM can help you build secure, automated operations with a unified end-to-end management solution, built on ibM Cloud Pak for Multicloud Management.

Will Cappelli, CTO of Moogsoft

Moogsoft provides iTops and devops teams with streamlined incident resolution capabilities to avoid outages, meet sLas, and help accelerate digital transformation. The Moogsoft aiops platform empowers teams to proactively identify and resolve incidents before they impact business services.

Moogsoft applies 50+ patented ai and machine learning algorithms to log, metric, trace, and alert data.

The results are less noise via effective correlation of actionable alerts,

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initiatives? reduction in the number of incident tickets created, and delivery of deep contextual insights that enable teams to solve iT problems faster.

The benefits of Moogsoft aiops include: ● Continuous service assurance ● improved responsiveness ● More uptime ● greater visibility ● system stability ● improved service delivery

The Moogsoft aiops platform features: 1. data ingestion at scale: Moogsoft applies ai & machine learning in real-time as it processes millions of log events, metrics, traces and alerts from existing monitoring tools.

2. Contextual enrichment: Moog-

soft integrates with critical information systems to add relevant contextual information to ingested events.

3. Noise reduction: Moogsoft separates the signal from the noise, calculating which events are significant and relevant.

4. event Correlation: Patented algorithms correlate events based on

Moogsoft AIOps Summary Dashboard – Alert Correlation

pattern discovery, then groups related events for full end-to-end context.

5. Probable root Cause: Moogsoft then identifies the impact and probable root cause of any issues, to aid troubleshooting efforts.

6. Team Collaboration: Moogsoft’s patented situation room is the place where teams collaborate to resolve incidents before they impact business services.

7. Workflow engine: Moogsoft can customize flexible and extensible resolution workflows, allowing teams full control of their data, where it goes, and when.

8. Knowledge recycle (similar situations): Moogsoft’s machine learning captures feedback and knowledge to calculate similar incidents, in real-time, as new incidents occur. Past resolution steps are documented in the knowledgebase.

9. integrations: Moogsoft aiops fully integrates with existing tools and infrastructure investments, including performance monitoring and iTsM. n September 2019

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Hybrid AI in the Future of Work

By Gabby Nizri

due to ongoing improvements in artificial intelligence and machine learning technologies, we are on the cusp of an entirely new era in automation. Not only are software robots adept at performing routine, repetitive tasks on behalf of humans, but they are now capable of carrying out activities that rely on cognitive abilities, such as those requiring the use of judgment and emotion. one only needs to look at the cars we drive to recognize just how far automation technology has come. does this mean that there will be no place for humans in the future?

The answer — at least for the foreseeable future — is a resounding no.

12 September 2019

That’s because, despite the growing list of benefits, there are also a number of drawbacks to having a system that is entirely autonomous. That’s where hybrid ai comes into play.

The concept behind hybrid ai is remarkably simple, even if the actual technologies and strategies driving it are incredibly complex. in basic terms, a hybrid model integrates humans throughout the automation process, but uses advanced technologies like deep learning and natural language processing to make automation systems even smarter.

AI needs humans

beyond the hype, the truth is that artificial intelligence technology is

simply not yet ready to replace humans — particularly when it comes to mission-critical applications.

Take, for example, Tesla’s autopilot feature. While the vehicle itself is equipped with the capability to drive on its own, the driver behind the wheel is still required to remain alert and attentive to ensure his or her safety. in other words, ai is capable of running unassisted, but when it comes to mission-critical functions, it still needs humans, not only to train it, but to make sure everything stays on track.

The truth is, when artificial intelligence gets things right, everything is peachy. but when it doesn’t, the outcome can be disastrous — especially for larger organizations. and while

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modern ai may have some impressive cognitive capabilities, at the end of the day, it’s still just as its name indicates: artificial. Keeping humans in the mix ensures that the nuances of communication are present and that the output is accurate and relevant.

Humans need AI

on the other side of the coin, humans can benefit tremendously from artificial intelligence technology. and with 37% of organizations having already implemented ai to some degree, it’s clear that people and machines working side by side is becoming the norm rather than the exception. The reason being, artificial intelligence is like a force multiplier for human workers.

for example, data mining can be handled far faster and in much more massive volumes than any human being is capable of. using ai, organizations can more effectively turn data into insights that can then be used to assist in human decisionmaking. This thereby drives innovation and competitive advantage. bringing it all together as we progress toward a more automated future, a hybrid approach to integrating ai can help organizations figure out how to get from point a to point b with as little business disruption as possible. one way executives are handling the shift is to create automation centers of excellence (Coe) that are dedicated to proliferating automation throughout

the organization. Taking a structured approach like this helps to reduce confusion and limit friction.

Members of the Coe are responsible for planning, ongoing testing and continuous oversight of the enterprise automation strategy. Typically, this group is made up of individuals who possess a mix of critical iT and business skills, such as developers, operations specialists and business analysts. additionally, an entirely new role of automation engineer is being created to support the Coe. Cios may choose to create their Coes with existing employees who are reskilled or newly hired team members. regardless, Coes represent a strategic approach that is designed to drive adoption across the enterprise while delivering key results in support of company goals. ultimately, choosing a hybrid approach that includes a combination of humans and artificial intelligence, is simply the logical evolution of any disruptive technology. it safeguards against the risks of early-stage gaps and helps organizations get the most out of new solutions every step of the way. done right, technology enables humans to focus on missioncritical applications while using ai to streamline operations and identify the best opportunities and strategies for ongoing organizational success. ai is not an either/or proposition. it’s up to each organization to determine the right mix of humans and technology that makes sense. as new capabilities and options emerge, that mix will inevitably evolve. and the iT leaders that fully embrace their increasingly strategic value will know how to create the balance that will continually optimize and elevate staff, technology and the entire future of work. n Gabby Nizri is co-founder and CEO of Ayehu.

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Survey: AIOps seen as delivering value

By Jenna Sargent

There has been a lot of talk about aiops in the past year, but it looks like the concept is now living up to expectations. aiops is no longer just a fancy marketing buzzword, explained bhanu singh, senior vice president of product development and cloud operations at opsramp. according to opsramp’s state of aiops report, a majority of aiops implementations have been successful. according to the report, 87 percent of respondents believe that aiops is delivering value through benefits such as improved hybrid infrastructure resilience, data-driven collaboration, and proactive iT operations. Less than a year ago, the company released a report revealing that 68 percent of organizations were starting to experiment with aiops.

“The opsramp state of aiops report matches with what we’re hearing from customers,” said singh. “aiops is emerging as a real-world solution to the data overload, infrastructure complexity, and incident remediation problems that are overwhelming digital operations teams in today’s enterprises.” according to the report, there are three main benefits of aiops: productivity gains from the elimination of repetitive tasks; faster root cause analysis, leading to rapid issue remediation; and better infrastructure performance through noise reduction.

The report also revealed some of the leading concerns surrounding aiops. sixty-seven percent of re14 September 2019

WHAT ARE THE PRIMARY OPERATIONAL BENEFITS OF USING AIOPS TOOLS? Automation of tedious tasks

85%

Suppression/de-duplication/ correlation of alerts Reduction in open incident tickets

80% 77% RESPONDENTS

WHAT CONCERNS DO YOU HAVE ABOUT THE USE OF AIOPS TOOLS?

67 %

Data accuracy Skill gaps (Data science, Machine learning, Inferential analysis) Errors/Loss of control Lengthy implementation cycles Jobs elimination

64% 52% 46% 39% Source: OpsRamp

RESPONDENTS

spondents are concerned about the accuracy and reliability of aiops tools, 64 percent feel there is a lack of the proper skills to support aiops deployments, and 52 percent are worried about the loss of control that aiops will bring. in order to alleviate those concerns, opsramps recommend teams spend time building trust in aiops recommendations and gain expertise in machine learning techniques. “aiops is going to exist. it is significantly going to help people with

productivity, the reliability of the environment, optimization and management of your system,” said singh. “How do you manage, how do you effectively deliver it? but at the same time, understand that you need to do some work in order to make aiops really, really effective in your organization.” opsramp conducted the survey with the help of a third party, and spoke to 200 iT managers across iT operations, devops, and site reliability engineering teams. n

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AIOps Exchange lays the groundwork for the future of IT operation a majority of companies have implemented or are starting to experiment with aiops, according to a survey from opsramp. of those, a majority of them have seen success with the aiops implementations.

The aiops exchange was launched earlier this year. it is a private forum committed to exchanging ideas, trends, and best practices that define the future of aiops, according to its website. its inaugural event was an extension of that mission. at the event, attendees were able to exchange ideas, discuss trends, and develop best practices that will define the future of aiops, the aiops exchange explained.

“We are just beginning to explore ai’s potential to transform iT operations Management and devops into a strategic activity at the core of digital business,” said Phil Tee, founder of aiops exchange and Ceo of Moogsoft. “at the aiops exchange, attendees will have the opportunity to discuss pressing topics with a group of industry experts and share best practices with their peers — everything from how to rollout aiops, quantifying the value of aiops, to re-skilling teams for aiops. even more importantly, attendees will help shape the direction this nascent industry takes over the years to come.”

Tee believes that unless the commercial world embraces ai as part of automation, it is really going to struggle to live up to expectations.

“With iT operational data volumes at an all-time high and showing no signs of slowing in scope and complexity, iT executive leaders must embrace technologies like ai/ML,” said Nancy gohring, senior analyst at 451 research. “These technologies can help solve immediate pain associated with iT operations data overload, such as alert fatigue and slow mean time to repair, and enable business critical projects like digital transformation.”

according to Tee, the switch to aiops will require a change in the way that organizations think about ai.

People need to be comfortable with ai being part of automation, he explained. “When we started Moogsoft seven years ago, there was a lot of resistance,” said Tee. “People were very nervous about the idea of machines doing the thinking for human beings around something as critical as which applications are serviced in response to an incident.” but over the last seven years, two things have changed to turn the tide. one is that the crisis has gotten worse because more companies are shifting to agile and implementing complexity in their workloads, requiring the use of ai. another factor is that ai has become more mainstream. Personal ai solutions like siri, alexa, and google Home weren’t around seven years ago, but now almost everyone has some form of ai in their lives, Tee explained.

“Companies tend to move when there’s a criticality of not doing it … usually when we get engaged with a customer, in the background there’s a story of service quality and some crisis around that, which has caused them to think again. i think over the course of the next few years as companies continue to expand their next-generation compute footprints, there will be more of that.” n

“We are just beginning to explore AI’s potential to transform IT Operations Management.”

—Phil Tee, founder of aiops exchange and Ceo of Moogsoft

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A guide to AIOps tools

n AppDynamics provides realtime monitoring of applications to detect anomalies. appdynamics helps break down silos by aligning business owners and iT departments by translating monitoring into business insights. it also offers an ecosystem of some of the top partners to complement and enhance companies’ investments in aiops. n BMC’s Truesight is a suite of

aiops solution that helps organizations reinvent their iT teams. it helps organizations continuously learn what the normal behaviors of their systems are so that they can detect when things aren’t normal. other benefits include the ability to reduce event noises, automatically generate incident tickets, and quickly drill down from business service level to application component tiers.

n FixStream is an aiops platform that facilitates automatic detection, prediction, and resolution of issues in hybrid iT stacks. The platform creates an accurate inventory of assets, automatically correlates millions of data points, and applies machine learning algorithms to detect patterns and predict issues. The company was recently acquired by resolve, and the two platforms will converge to help customers achieve “self-healing iT.” n Instana provides aPM for or-

ganizations. it offers automatic visualization and performance analysis across a company’s entire application stack. its ai-assisted troubleshooting helps automatically identify what the most likely trigger of an incident is.

n Micro Focus operations bridge (opsbridge) helps transform iT operations into business partners. opsbridge consolidates data into a dynamically updated and accurate 16 September 2019

FEATURED PROVIDERS

n IBM helps organizations modernize their iT operations management with its aiops solution. it helps organizations see patterns and contexts that aren’t obvious, helping them avoid investigation, improve responsiveness, and lower operations costs. it also automates iT tasks to minimize the need for human intervention. ibM’s solution can be incorporated no matter what stage in the digital transformation journey a customer is at.

n Moogsoft is a pioneer and leading provider of aiops solutions that help iT teams work faster and smarter. With patented ai analyzing billions of events daily across the world’s most complex iT environments, the Moogsoft aiops platform helps the world’s top enterprises avoid outages, automate service assurance, and accelerate digital transformation initiatives. founded in 2011, Moogsoft has more than 120 customers worldwide and strategic partnerships with leading managed service providers and outsourcing organizations.

model of what impacts what in an environment. it also offers over 200 integrations that help automate discovery and monitoring of logs, events, metrics, and topologies. n New Relic recently entered the aiops space with the acquisition of signifai earlier this year. New relic acquired the company because its ai vision aligned with its own vision for ai. for New relic, it’s important to aiops solves real customer problems, and does it quickly. n OpsRamp provides an aiops

solution that is service-centric. its solution helps iT teams serve the business across all departments, business units, and locations. With opsramp, customers can consolidate hybrid infrastructure for maximum visibility, map infrastructure to business services, and automate multi-cloud management using machine learning. n Optanix’ aiops platform was developed from the group up, rather than just adding an analytics engine

or machine learning capabilities to an existing platform. The solution offers full-stack detection and monitoring, predictive analysis and smart analytics, true and actionable root cause analysis, and business service prioritization.

n ScienceLogic offers a “context-infused” aiops platform that helps organizations discover and understand the relationship between infrastructure, applications, and business services. it also allows them to integrate and share data across different technologies in real-time, and apply multi-directional integrations for automating responsive and proactive actions in the cloud.

n StackState’s aiops platform helps iT operations teams break down silos in their teams and tools. its solution combines logs, events, metrics, and traces in real time in order to help customers resolve issues faster. With stackstate, organizations can consolidate all of their data into a single platform with an understandable ui. n