1. There are four steps IT leaders can take now to get AI out of ‘pilot purgatory.’
An article in VentureBeat shows the framework for organizations to get AI from a concept developed with perfect conditions to a practical tool that can make any business better by learning by using real-world data to deliver faster and better results.
The global AI race is accelerating, and if we do not invest now in ways to scale AI more effectively, we risk falling behind. That is why now is the time to get AI out of “pilot purgatory” and into practical implementation. The article goes on to explain the role of AIOps in scaling AI by developing a framework that includes mission engineering, data operations, and reliability engineering.
The four steps that will yield success and scalability for an AIOps framework are:
- Establish your AI vision.
- Begin to articulate AI’s potential impact.
- Identify your AI champions.
- Capitalize on quick wins.
By strategically leveraging AIOps through a comprehensive, proven framework, IT leaders can close the chasm between conceptual innovation and real-world deployment, helping the U.S. stay ahead in the global race for AI supremacy.
2. Brush up on the top 10 tech buzzwords for IT operations teams in 2022.
This article in ITProToday listed and explained the most important terms that are expected to be essential this year. By familiarizing yourself with these terms you will be in-the-know of what the experts are talking about.
Here are 10 that are buzzing around right now:
- Everything as code
- Multi-cluster
- NoOps
- AIOps
- Chaos Engineering
- Site reliability engineering
- Software supply chain
- Least privilege
- Zero trust
- Microsegmentation
3. AIOps combines machine learning and automation to transform IT operations.
AIOps has been proven to provide greater efficiencies with reduced operational costs for cloud-based companies. This article in CIO explains how AIOps makes organizations better.
A healthy IT organization can provide key competitive advantages for businesses in a fast-paced market. Many companies struggle to meet the high demand due to increased cloud system complexity. Distributed apps (where distinct parts of an app run on different systems) make it difficult to track where problems occur during an IT incident. Every minute of downtime or app failure directly impacts revenues.
To mitigate these failures, IT organizations have ballooned in size. Increased cloud investments demand people that can do everything: build efficient systems, scale them to millions of users, and plug holes that lead to downtime.
AIOps practices save companies time and money. ITOps teams can spend time building scalable systems, rather than chasing down noisy alerts and doing redundant tasks. Cloud systems gain efficiency thanks to reduced app downtime. AIOps platforms predict potential IT incidents and resolve them without human intervention.
4. Discover the difference between DevOps, MLOps and DataOps.
An article in DevOps.com breaks down the differences and significance of the three distinct disciplines of DevOps, MLOps and DataOps.
Soon, almost every piece of software we interact with will have intelligence built-in, and as AI gains acceptance and new use cases are rolled out, applications will only continue to become more intelligent.
But innovation is rarely easy or simple—and intelligent applications are no exception. While conventional software involves one thing—code—and not much else, intelligent software relies on a complex relationship between three interconnected variables or legs of a three-legged stool.
The components of this stool are model, data, and code. If you try to remove any one of them, the application will topple over.
With the three distinct pillars that make up intelligent applications, three distinct disciplines have emerged to keep each individual pillar functioning as efficiently as possible: DataOps, MLOps, and DevOps.
Each of these fields is distinct, dealing with a separate set of questions and objectives within the ML life cycle and requiring diverse kinds of people and tools. However, they are all fundamentally united by a common goal: Optimizing quality and speed of iteration of the ML life cycle.
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