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 * News
 * Sports
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 * news
   
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    * Top Stories
    * Local
    * Climate
    * Currently Selected: World
    * Canada
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MOST IMPORTANTLY


HOW AI IS CHANGING THE FUTURE OF IT OPERATIONS

Today’s IT environments are generating a skyrocketing volume of data and alerts
that can become a management nightmare for IT teams. To help solve the problem,
IT leaders are applying artificial intelligence (AI) to enhance IT operations.

As organizations transform, their infrastructures are becoming more dynamic and
distributed. “It’s a mix of on-premises and multi-cloud environments running on
bare metal, virtualized servers and containers said Rodrigo de la Parra, AIOps
Domain Leader at IBM at a recent CanadianCIO Virtual Roundtable. It’s a
challenge for domain-based tools to correlate the data across these different
environments so that IT teams can respond to issues fast enough to meet business
needs. “We have to reduce the noise and empower operations teams to do what they
do best,” said de la Parra. By deploying AI in IT operations (AIOps), it allows
IT to optimize the capacity of its operations. “Our goal is to avoid issues
proactively by providing context and promote collaboration among siloed teams.
This eliminates manual labour-intensive work so the team can focus on more
valuable projects that accelerate transformation.” Indeed, according to Gartner,
“there is no future of IT operations that does not include AIOps.”


AIOPS EXPLAINED

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AIOps is a platform that leverages AI, machine learning, big data from different
sources, automation and natural language processing, explained de la Parra. It
complements the multiple separate, manual IT operations tools with a cohesive,
intelligent, and automated IT operations platform. As a first step, the AI
solution collects and aggregates the ever-increasing volumes of operations data.
The system relies on a centralized data lake as a “single source of truth,” said
de la Parra. He noted that it can analyze structured and unstructured data, like
Twitter feeds, with no need for tagging. The advantage is that the platform
provides visibility into performance data across all environments As well, AIOps
sifts through the data to identify significant events and pattern anomalies
related to system performance. It will diagnose root causes and report them to
IT for a rapid response. As the system and the machine learning matures, AIOps
can solve issues without human intervention, de la Parra said. “It is a domain
agnostic platform that provides a holistic view and identifies issues in
real-time,” he said. “There are no blind spots.” 3 How AI is changing the future
of IT operations The AI models use the data source to create a baseline of the
way that a system is performing normally so that it can spot any deviations.
This is more effective than a rules-based approach which can no longer cope with
hybrid multi-cloud environments. When AIOps is combined with automation, “it
allows IT teams to scale mundane activities efficiently,” said de la Parra.


IMPLEMENTING AI OPS

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In a poll during the roundtable discussion, 60 per cent of the participants said
that they are not currently using AI technology to support IT operations.
However, they said they’re interested in learning about the options. Thirteen
per cent said they’re just starting to implement it, while seven per cent said
they’re using it to diagnose and solve IT outages. Another seven per cent said
they have no plans to integrate AI into their IT operations. As the poll
demonstrates, most Canadian organizations are at the exploratory stage on AIOps,
said de la Parra. Typically, they move forward when scalability becomes an
issue. He described two models organizations use as they begin to implement
AIOps:

1. Domain-centric solutions. The entry point for many organizations is to
upgrade traditional IT tools with the vendor’s AI infused, and rules-based
machine learning versions. “This reduces noise, but it’s a starting point that
will only take you so far,” de la Parra said. It provides a view of some issues,
but usually across only a siloed domain (e.g. app & networking performance
management, security, storage, middleware, service management, etc). As such, it
doesn’t give visibility across all environments, such as hybrid multi-cloud,
leaving potential critical blind spots.

2. General purpose or domain agnostic platform. This approach provides a
holistic view and the ability to identify issues in real-time as the environment
becomes more complex by analyzing feeds from multiple data-sources, including
historic and real-time streaming, correlating relevant information and providing
the next best action to resolve incidents faster via ChatOps. It provides a 360
degree view of the entire system and is more useful as environments become more
complex.

“We recommend that a good place to start is by applying AIOps to an application
that has many issues that IT is struggling to support where business impact is
significant,” said de la Parra. This is a good way to start to train the machine
learning systems to detect and predict anomalies. We recommend that a good place
to start is by applying AIOps to an application that has many issues that IT is
struggling to support where business impact is significant. — Rodrigo de la
Parra, AIOps Domain Leader, IBM 4 How AI is changing the future of IT operations
One participant expressed concern about the length of time it could take to set
up the AI and machine learning models. Typically, the IBM Watson AIOps platform
can be evaluated in three or four weeks, de la Parra responded, where training
the model can be achieved in several hours or days depending on the feed(s)
size(s) and delivery latency. It has a high degree of accuracy for finding
anomalies based on the quality and completeness of the data sources provided by
using varying out of the box techniques.


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