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AI

What the growth of AIops solutions means for the enterprise

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Without exaggeration, digital transformation is moving at breakneck speed, and the verdict is that it will only move faster. More organizations will migrate to the cloud, adopt edge computing and leverage artificial intelligence (AI) for business processes, according to Gartner.

Fueling this fast, wild ride is data, and this is why for many enterprises, data — in its various forms — is one of its most valuable assets. As businesses now have more data than ever before, managing and leveraging it for efficiency has become a top concern. Primary among those concerns is the inadequacy of traditional data management frameworks to handle the increasing complexities of a digital-forward business climate.

The priorities have changed: Customers are no longer satisfied with immobile traditional data centers and are now migrating to high-powered, on-demand and multicloud ones. According to Forrester’s survey of 1,039 international application development and delivery professionals, 60% of technology practitioners and decision-makers are using multicloud — a number expected to rise to 81% in the next 12 months. But perhaps most important from the survey is that “90% of responding multicloud users say that it’s helping them achieve their business goals.”

Managing the complexities of multicloud data centers

Gartner also reports that enterprise multicloud deployment has become so pervasive that until at least 2023, “the 10 biggest public cloud providers will command more than half of the total public cloud market.”

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But that’s not where it ends — customers are also on the hunt for edge, private or hybrid multicloud data centers that offer full visibility of enterprise-wide technology stack and cross-domain correlation of IT infrastructure components. While justified, these functionalities come with great complexities. 

Typically, layers upon layers of cross-domain configurations characterize the multicloud environment. However, as newer cloud computing functionalities enter into the mainstream, new layers are required — thus complicating an already-complex system.

This is made even more intricate with the rollout of the 5G network and edge data centers to support the increasing cloud-based demands of a global post-pandemic climate. Ushering in what many have called “a new wave of data centers,” this reconstruction creates even greater complexities that place enormous pressure on traditional operational models. 

Change is necessary, but considering that the slightest change in one of the infrastructure, security, networking or application layers could result in large-scale butterfly effects, enterprise IT teams must come to terms with the fact that they cannot do it alone.

AIops as a solution to multicloud complexity

Andy Thurai, VP and principal analyst at Constellation Research Inc., also confirmed this. For him, the siloed nature of multicloud operations management has resulted in the increasing complexity of IT operations. His solution? AI for IT operations (AIops), an AI industry category coined by tech research firm Gartner in 2016.

Officially defined by Gartner as “the combination of big data and ML [machine learning] in the automation and improvement of IT operation processes,” the detection, monitoring and analytic capabilities of AIops allow it to intelligently comb through countless disparate components of data centers to provide a holistic transformation of its operations. 

By 2030, the rise in data volumes and its resulting increase in cloud adoption will have contributed to a projected $644.96 billion global AIops market size. What this means is that enterprises that expect to meet the speed and scale requirements of growing customer expectations must resort to AIops. Else, they run the risk of poor data management and a consequent fall in business performance. 

This need creates a demand for comprehensive and holistic operating models for the deployment of AIops — and that is where Cloudfabrix comes in.

AIops as a composable analytics solution

Inspired to help enterprises ease their adoption of a data-first, AI-first and automate-everywhere strategy, Cloudfabrix today announced the availability of its new AIops operating model. It is equipped with persona-based composable analytics, data and AI/ML observability pipelines and incident-remediation workflow capabilities. The announcement comes on the heels of its recent release of what it describes as “the world-first robotic data automation fabric (RDAF) technology that unifies AIops, automation and observability.”

Identified as key to scaling AI, composable analytics give enterprises the opportunity to organize their IT infrastructure by creating subcomponents that can be accessed and delivered to remote machines at will. Featured in Cloudfabrix’s new AIops operating model is a composable analytics integration with composable dashboards and pipelines.

Offering a 360-degree visualization of disparate data sources and types, Cloudfabrix’s composable dashboards feature field-configurable persona-based dashboards, centralized visibility for platform teams and KPI dashboards for business-development operations. 

Shailesh Manjrekar, VP of AI and marketing at Cloudfabrix, noted in an article published on Forbes that the only way AIops could process all data types to improve their quality and glean unique insights is through real-time observability pipelines. This stance is reiterated in Cloudfabrix’s adoption of not just composable pipelines, but also observability pipeline synthetics in its incident-remediation workflows.

In this synthesis, likely malfunctions are simulated to monitor the behavior of the pipeline and understand the probable causes and their solutions. Also included in the incident-remediation workflow of the model is the recommendation engine, which leverages learned behavior from the operational metastore and NLP analysis to recommend clear remediation actions for prioritized alerts. 

To give a sense of the scope, Cloudfabrix’s CEO, Raju Datla, said the launch of its composable analytics is “solely focused on the BizDevOps personas in mind and transforming their user experience and trust in AI operations.”

He added that the launch also “focuses on automation, by seamlessly integrating AIops workflows in your operating model and building trust in data automation and observability pipelines through simulating synthetic errors before launching in production.” Some of those operational personas for whom this model has been designed include cloudops, bizops, GitOps, finops, devops, DevSecOps, Exec, ITops and serviceops.

Founded in 2015, Cloudfabrix specializes in enabling businesses to build autonomous enterprises with AI-powered IT solutions. Although the California-based software company markets itself as a foremost data-centric AIops platform vendor, it’s not without competition — especially with contenders like IBM’s Watson AIops, Moogsoft, Splunk and others.

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Categories
AI

Why AIops may be necessary for the future of engineering

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Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. 

The question is — what role is artificial intelligence (AI) going to play in engineering? How will the future of building and deploying code be impacted by the advent of ML? Here, we’ll argue why ML is becoming central to the ongoing development of software engineering.

The growing rate of change in software development

Companies are accelerating their rate of change. Software deployments were once yearly or bi-annual affairs. Now, two-thirds of companies surveyed are deploying at least once a month, with 26% of companies deploying multiple times a day. This growing rate of change demonstrates the industry is accelerating its rate of change to keep up with demand.

If we follow this trend, almost all companies will be expected to deploy changes multiple times a day if they wish to keep up with the shifting demands of the modern software market. Scaling this rate of change is hard. As we accelerate even faster, we will need to find new ways to optimize our ways of working, tackle the unknowns and drive software engineering into the future.

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Enter machine learning and AIops

The software engineering community understands the operational overhead of running a complex microservices architecture. Engineers typically spend 23% of their time undergoing operational challenges. How could AIops lower this number and free up time for engineers to get back to coding?

Utilizing AIops for your alerts by detecting anomalies

A common challenge within organizations is to detect anomalies. Anomalous results are those that don’t fit in with the rest of the dataset. The challenge is simple: how do you define anomalies? Some datasets come with extensive and varied data, while others are very uniform. It becomes a complex statistical problem to categorize and detect a sudden change in this data.

Detecting anomalies through machine learning

Anomaly detection is a machine learning technique that uses an AI-based algorithm’s pattern recognition powers to find outliers in your data. This is incredibly powerful for operational challenges where, typically, human operators would need to filter out the noise to find the actionable insights buried in the data.

These insights are compelling because your AI approach to alerting can raise issues you’ve never seen before. With traditional alerting, you’ll typically have to pre-empt incidents that you believe will happen and create rules for your alerts. These can be called your known knowns or your known unknowns. The incidents you’re either aware of or blind spots in your monitoring that you’re covering just in case. But what about your unknown unknowns

This is where your machine learning algorithms come in. Your AIops-driven alerts can act as a safety net around your traditional alerting so that if sudden anomalies happen in your logs, metrics or traces, you can operate with confidence that you’ll be informed. This means less time defining incredibly granular alerts and more time spent building and deploying the features that will set your company apart in the market.

AIops can be your safety net

Rather than defining a myriad of traditional alerts around every possible outcome and spending considerable time building, maintaining, amending and tuning these alerts, you can define some of your core alerts and use your AIops approach to capture the rest.

As we grow into modern software engineering, engineers’ time has become a scarce resource. AIops has the potential to lower the growing operational overhead of software and free up the time for software engineers to innovate, develop and grow into the new era of coding.

Ariel Assaraf is CEO of Coralogix.

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Categories
AI

How AIOps can benefit businesses

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“AIOps,” which stands for “AI for IT operations,” refers to the way data and information from a dev environment is managed by an IT team — in this case, using AI. AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions with proactive and personal insights, enabling the use of multiple data sources and data collection methods. In theory, AIOps can provide faster resolutions to outages and other performance problems, in the process decreasing the costs associated with IT challenges.

The benefits of AIOps are driving enterprise adoption. Eighty-seven percent of respondents to a recent OpsRamp survey agree that AIOps tools are improving their data-driven collaboration, and Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023.

But when deploying an AIOps solution, businesses without a clear idea of potential blockers can run into challenges. That’s why it’s important to have a holistic understanding of AIOps before formulating a strategy.

What is AIOps?

AIOps platforms collect data from various IT operations tools in order to automatically spot issues while providing historical analytics. They typically have two components — big data and machine learning — and require a move away from siloed IT data in order to aggregate observational data alongside the engagement data in ticket, incident, and event recording.

As Seth Paskin, director of operations at BMC Software, writes: “The outcomes IT professionals expect from AIOps can be categorized generally as automation and prediction … Their first expectation from AIOps is that it will allow them to automate what they are currently doing manually and thus increase the speed at which those tasks are performed. Some specific examples I’ve heard include: correlate customer profile information with financial processing applications and infrastructure data to identify transaction duration outliers and highlight performance impacting factors; evaluate unstructured data in service tickets to identify problem automation candidates; categorize workloads for optimal infrastructure placement; and correlate incidents with changes, work logs, and app dev activities to measure production impact of infrastructure and application changes.”

An AIOps platform canvasses data on logs, performance alerts, tickets, and other items using an auto-discovery process that automatically collects data across infrastructure and application domains. The process identifies infrastructure devices, running apps, and business transactions and correlates all the data in a contextual form. Automatic dependency mapping determines the relationships between elements such as the physical and virtual connections at the networking layer by mapping app flows to the supporting infrastructure and between the business transactions and the apps.

AIOps’ automated dependency mapping has another benefit: helping to track relationships between hybrid infrastructure entities. AIOps platforms can create service and app topology maps across technology domains and environments, allowing IT teams to accelerate incident response and quantify the business impact of outages.

To identify patterns and predict future events, like service outages, AIOps employs supervised learning, unsupervised learning, and anomaly detection based on expected behaviors and thresholds. Particularly useful is unsupervised machine learning, which enables AIOps platforms to learn to recognize expected behavior and set thresholds across data and performance metrics. The platforms can analyze event patterns in real time and compare those to expected behavior, alerting IT teams when a sequence of events (or groups of events) demonstrates activity that indicates anomalies are present.

The insights from AIOps platforms can be turned into a range of intelligent actions performed automatically, from expediting service desk requests to end-to-end provisioning to deployment of network, compute, cloud, and applications. In sum, AIOps brings together data from both IT operations management and IT service management, allowing security teams to observe, engage, and act on issues more efficiently than before.

Challenges

Not every AIOps deployment goes as smoothly as planned. Challenges can stand in the way, including poor-quality data and IT team errors. Employees sometimes face difficulty in learning how to use AIOps tools, and handing over control to autonomous systems can pose concerns among the C-Suite. Moreover, adopting new AIOps solutions can be time-consuming — a majority of respondents to the OpsRamp survey said it takes three to six months to implement an AIOps solution, with 25% saying that it takes greater than six months.

Because AIOps platforms rely so heavily on machine learning, challenges in data science can impact the success of AIOps strategies. For example, getting access to quality data to train machine learning systems isn’t easy. According to a 2021 Rackspace Technology survey, poor data quality was the main reason for machine learning R&D failure among 34% of respondents. Thirty-one percent said they lacked production-ready data.

Beyond data challenges, the skills gap also presents a barrier to AIOps adoption. A majority of respondents in a 2021 Juniper report said their organizations were struggling with expanding their workforce to integrate with AI systems. Laments over the AI talent shortage have become a familiar refrain from private industry — O’Reilly’s 2021 AI Adoption in the Enterprise paper found that a lack of skilled people and difficulty hiring topped the list of challenges in AI, with 19% of respondents citing it as a “significant” blocker.

Unrealistic expectations from the C-suite are another top reason for failure in machine learning projects. While 9 in 10 of C Suite survey respondents characterized AI as the “next technological revolution,” according to Edelman, Algorithmia found that a lack of executive buy-in contributes to delays in AI deployment.

Benefits

Successfully adopting AIOps isn’t a sure-fire thing, but many businesses find the benefits worth wrestling with the challenges. AIOps systems reduce the torrent of alerts that inundate IT teams and learn over time which types of alerts should be sent to which teams, reducing redundancy. They can be used to handle routine tasks like backups, server restarts, and low-risk maintenance activities. And they can predict events before they occur, such when network bandwidth is reaching its limit.

As Accenture explains in a recent whitepaper, AIOps ultimately improves an IT organization’s ability to be an effective partner to the business. “An IT operations platform with built-in AIOps capabilities can help IT operations proactively identify potential issues with the services and technology it delivers to the business and correct them before they become problems,” the consultancy wrote. “That’s the value of having a single data model that service and operations management applications can share seamlessly.”

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AI

Moogsoft rolls out new AIOps features

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AIOps startup Moogsoft this week announced the launch of new product capabilities and updates, including extended integration capabilities, incident workflow automations, and automatic event enrichment. Moogsoft says the features are designed to optimize productivity while enabling developers to focus their time on more involved tasks.

AI for IT operations (AIOps) is a category of products that enhance IT by leveraging AI to analyze data from tools and devices. Research and Markets anticipates AIOps will become a $14.3 billion market by 2025. That might be a conservative projection, in light of the pandemic, which is increasingly forcing IT teams to conduct their work remotely. In lieu of access to infrastructure, AIOps solutions could help prevent major outages, which a study from Aberdeen Research estimates cost $260,000 per hour.

Moogsoft upgraded its Create Your Own Integration service, which lets customers generate custom API endpoints they can send event and metric data to from observability and monitoring tools. Now users have the ability to integrate payloads that include multiple events, ostensibly saving time and resources.

Also new in beta is Microsoft Azure App Insights integration, a cloud-to-cloud integration that provides admins the ability to ingest metric data configured in Azure. Moogsoft analyzes the data to establish normal operating behaviors with an upper and lower threshold. If the metric deviates, an anomaly event occurs, which allows users to understand the difference between normal behavior and a potential issue.

AI-powered features

Auto Classify, another new feature in beta, taps machine learning to analyze data and determine infrastructure elements and types of failure. Meanwhile, the similarly AI-powered Auto Close extends workflow automation to automatically close any alerts or incidents when they meet the criteria users set. For example, when a metric falls back into normal behavior, the alert severity moves to “clear” and the status to “resolved.”

Dovetailing with Auto Classify and Auto Close is automated incident context with tags, which aggregates and deduplicates tags from alerts, including custom tags. New personalization capabilities are also in tow and allow users to change the order of columns, which columns are displayed, and the refresh rate of their UI.

“In a period of immense growth, our team is working harder than ever to allow development, ITOps, and SRE teams to work more efficiently,” Moogsoft VP Adam Frank told VentureBeat. “Simplicity within the user experience continues to be at the forefront of everything we release — from new and improved navigation to the ease-of-use of our industry-leading open and transparent integrations — making it even easier for our customers to take advantage of observability with AIOps and allowing them to develop more and operate less.”

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AI

IBM is acquiring Turbonomic to advance AIOps agenda

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IBM announced this week that it is acquiring Turbonomic, provider of application resource management (ARM) and network performance management (NPM) software infused with machine learning algorithms. Terms of the acquisition, which is expected to close this quarter, were not disclosed.

The two companies have a long-standing relationship under which IBM has been reselling Turbonomic’s ARM platform. Cisco also resells tools developed by the company. Turbonomic, which is privately held, claims revenues were up 41% for fiscal 2021 and counts Avon, HauteLook, and Litehouse Foods among its customers.

Applications and systems management

The decision to acquire Turbonomic comes after IBM began revamping its application and systems management portfolio last fall. This push began in earnest with the acquisition of Instana, provider of an application performance management (APM) platform for monitoring and observing applications.

IBM now plans to further integrate the ARM software Turbonomic developed with the APM software from Instana and an IBM Cloud Pak for Watson AIOps platform that employs machine learning algorithms to identify anomalies in real time.

“Turbonomic provides actionable observability,” IBM Automation GM Dinesh Nirmal told VentureBeat in an interview.

IBM is further extending its IT management portfolio via the recent acquisition of WDG Automation, provider of a robotic process automation (RPA) platform, and MyInvenio, which offers process mining tools, he noted.

As IT environments become more complex, Nirmal said it won’t be feasible to manage these environments without augmenting IT staff with capabilities enabled by AI platforms. It’s not likely AI platforms will replace the need for human IT administrators, but the job functions themselves will continue to evolve as lower-level manual tasks become automated, Nirmal added.

IT challenges

Now that companies are becoming more cognizant of the scope of IT management challenges, IT teams are increasingly embracing AI platforms. Organizations are now deploying a new generation of microservices-based applications that are more difficult to manage than the existing monolithic legacy applications, which are not likely to be retired anytime soon, Nirmal said. Those applications make use of cloud-native technologies such as containers, Kubernetes, and serverless computing frameworks that all need to be managed alongside virtual machines. At the same time, the IT environment has become more distributed than ever, thanks to the rise of both cloud and edge computing platforms.

The only way to contain the total cost of managing that extended enterprise is to rely more on automation enabled by AIOps platforms, Nirmal said.

IT teams need to come to terms with the fact that it takes time for machine learning algorithms to learn IT environments that are unique and subject to change. Implementing AI requires patience, Nirmal said, adding, “IT teams need to accept that AI comes with an upfront cost.”

But the return on investment in AIOps becomes apparent as rote tasks are eliminated and more potential issues are addressed before they impact an application, Nirmal noted. IT teams, for example, will be able to predict the impact new code is likely to have on the overall IT environment before it’s deployed.

IBM’s investments in AIOps are a natural extension of the capabilities IBM has developed to automate a wide range of business processes using AI technologies, Nirmal added. IT leaders can’t make a credible case for applying AI to automate business processes if the IT team isn’t using the same technologies to automate IT operations, he noted.

At this juncture, AI is about to become a mainstream component of IT operations. The issue now is determining to what degree. In some cases, AI capabilities will be slipstreamed into existing platforms, while in others, IT teams will decide to move to a new platform. Either way, machine learning algorithms will be present in one form or another.

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AI

Device42 extends AIOps reach of IT infrastructure discovery tool

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Device42 today announced it has added support for additional platforms to an AIOps tool that enables organizations to discover IT resources residing in the cloud and in on-premises IT environments.

As enterprise IT environments become more extended, platforms are being added in a way that is not always immediately discernible to a centralized IT team. Developers, for example, now routinely spin up virtual machines on public clouds without any intervention on the part of an internal IT team required.

AIOps tools from Device42 employ machine learning algorithms to create device and application topologies and impact charts that surface all the IT infrastructure being employed across a hybrid cloud computing environment. The latest update adds the ability to also discover cloud databases, along with other types of cloud services — in addition to storage resources in on-premises IT environments.

Those assets are discovered using the application programming interfaces (APIs) that have been exposed by cloud services providers and IT vendors that provide infrastructure for on-premises IT environments. Once all those assets are mapped, it becomes possible for IT teams to employ a search function to uncover, for example, dependencies between multiple services being employed, Device42 cofounder and CEO Raj Jalan told VentureBeat.

Challenges at scale

Not every IT organization may need an AI platform to keep track of its IT assets. But the larger an organization becomes, the more challenging it is to monitor what infrastructure is being employed where and for what purpose. Cloud services, especially, tend to be dynamically consumed, which makes trying to keep track of usage manually nearly impossible. It’s not uncommon for IT organizations to find themselves being billed for cloud services they had no idea where being employed until the invoice from the cloud service provider arrived.

At the same time, more application workloads are now starting to be pushed out to the network edge in places centralized IT teams often have no way to physically reach.

With the addition of each new platform to an IT environment, the probability an organization is going to run afoul of one compliance regulation or another increases, Jalan noted. The Device42 platform makes it easier for IT teams to uncover potential issues long before any audit might, he said.

This capability can also play a role in helping IT teams discover application workloads they may want to move from on-premises IT environments to the cloud or vice versa.

Future of AIOps

Overall, the rate of change in IT environments is accelerating as organizations add, for example, Kubernetes clusters to run microservices-based applications that are being rolled out by a line-of-business unit. IT organizations now need AIOps capabilities to keep track of the changes and updates being made across an extended enterprise. “IT has become too complex to run without it,” he said.

Longer-term, it’s not clear to what degree AIOps will remain a discrete concept or simply become part of the IT service management (ITSM) firmament. At some point, every ITSM tool is going to be infused with machine learning algorithms to some degree. Of course, many IT professionals are dubious about the capabilities of AIOps platforms. But the longer machine learning algorithms are employed, the more accurate they become since they continuously learn from their environment.

As the overall size of the IT environment continues to expand, increasing the size of the IT staff needed to manage it become financially impractical. It’s now only a question of to what degree AI will be employed to augment IT teams that have no other viable way of managing modern IT environments.

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Categories
AI

ScienceLogic raises $105 million to grow its AIOps platform

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ScienceLogic, a startup developing an AIOps and IT infrastructure monitoring platform, today announced that it raised $105 million. The company says the investment will support its continued growth in the AIOps market and further broaden ScienceLogic’s position within the IT operations management industry.

AIOps, short for AI for IT operations, is a category of products that enhance IT by leveraging AI to analyze data from tools and devices. Research and Markets anticipates it’ll be a $14.3 billion market by 2025. That might be a conservative projection in light of the pandemic, which is forcing IT teams to increasingly conduct their work remotely. In lieu of access to infrastructure, AIOps solutions could help prevent major outages, the cost of which a study from Aberdeen Research pegged at $260,000 per hour.

Reston, Virginia-based ScienceLogic, which was founded in 2003, uses AI-driven techniques to discover technologies and vendors across physical, virtual, and cloud IT environments. It collects and stores a range of analytics data in a clean, normalized data lake, applying algorithms to spotlight relationships between infrastructure applications and enterprise business services. With ScienceLogic, IT teams can integrate and share data across different technologies. Moreover, they can apply integrations to automate actions in real time.

For example, ScienceLogic can automatically extract, populate, and synchronize information with an asset management solution, whether ScienceLogic’s own tool or a third-party product like ServiceNow. The company’s platform maps multicloud infrastructure, identifying dependencies among IT elements. And it notes events in logs from apps and network performance management software, ultimately leading to deeper problem diagnosis.

ScienceLogic

Above: ScienceLogic’s monitoring dashboard.

Image Credit: ScienceLogic

CEO Dave Link says that one ScienceLogic customer, Kellogg, managed to increase monitoring coverage for their infrastructure by 500% while saving $2 million in software costs over a five-year period. Using ScienceLogic’s platform, Kellogg now monitors its IT environment onsite and offsite in Amazon Web Services with one-fifth of the staff that a legacy solution might have required. Kellogg’s previous monitoring solution would have cost $10 million and required 10 technicians to scale to all of the company’s locations around the world.

“More than ever, IT Operations Management has taken root as a front-office priority supporting mission-critical digital experiences that define the way we live, work and play. As large enterprises shift workloads to the cloud while managing on-prem resources, new tools are paramount to deliver service visibility and faster incident resolutions made better by advanced AI and machine learning technologies,” Link said in a press release. “What we’re witnessing is a major investment cycle away from legacy monitoring tools and toward AIOps platforms.”

ScienceLogic claims that some of the largest global enterprises, federal agencies, and managed service providers are among the thousands of customers who use its platform. With the proceeds from the series E funding round announced today, which was led by Silver Lake Waterman with participation from existing investors Goldman Sachs, Intel Capital, and NewView Capital, ScienceLogic plans to invest in product development and engineering as well as hiring.

Reston, Virginia-based ScienceLogic has a substantial war chest with over $200 million raised to date, but it competes with a number of startups and incumbents in the competitive AIOps space. OpsRamp, whose product applies AI to DevOps processes, recently raised $37.5 million. Carbon Relay nabbed $63 million last February to further develop its AI-powered monitoring platform. There’s also IBM, which in May debuted Watson AIOps, a solution designed to automate IT anomaly detection and remediation.

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