IT Ops at The Core: A Brief History and Overview
In this blog
In this article, every IT operation known to man will be examined in detail. The list includes DevOps, MLOps, AIOps, ITOps, DevSecOps, AppOps, InfraOps, SecOps, NetOps, IDoubtYourStillReadingThisOps, DataOps, ChatOps and more.
On second thought, let us not bore you away. The purpose of this article is to introduce the reader to the vast landscape of IT operations. Rather than provide an exhaustive list, this article summarizes the history of IT operations, examines Ops at their core, and gives a high-level overview of DevOps, MLOps and AIOps.
The term "Ops" is an abbreviation of the word operations, and in the business world, this usually refers to operations management. However, Ops more specifically refers to IT Operations in the tech world. The blend of "Ops" and other fields started around the 1990s with the development and rising popularity of the agile practice, a new methodology in software development. Agile is an alternative to the waterfall model of software development, which is a slow, step-by-step process for getting from building to testing to production. The agile approach intended to speed this process by focusing on building and testing small aspects of the product to have them quickly pushed to consumers for feedback, hence the name "agile." The success of this practice led to the development of one of the first blends of "Ops" and other fields, namely, DevOps, which is the blend of software development and IT operations.
An adjective is an attribute that describes a noun. Agile is not a noun or a verb but an adjective. This distinction is especially important because you cannot purchase or perform agile. The term agile refers to a set of principles envisioned in 2001 as a common ground for software development. Nowadays, agile can mean a variety of things. An agile workflow typically involves one to three weeks of work and frequent check-ins. However, there is a little more to it than that. You can read through the manifesto for agile software development, the 12 principles of agile software development and its history.
Simply put, here are the steps to being agile:
- Find out where you are.
- Take a small step toward your goal.
- Adjust your understanding based on what you have learned.
- When faced with two or more alternatives that deliver roughly the same value, take the path that makes future change easier.
- Repeat steps 1-4.
When a team is trying to become more agile, it is very common for them to search for the best agile tools. Similarly, teams attempt to replicate the successful agile workflow of another team. Yes, there are useful agile tools. And yes, some agile practices are better than others. But the key to being agile is not necessarily the tools or practices themselves; it is the team's ability to make decisions quickly. Considering that tools and practices themselves will change, it is important for teams to understand that the culture of quickly making good decisions is more important than the tools and practices themselves.
IT Ops encompass People, Process, and Technology. At their core, Ops are outcome-driven cultures focused on agility while maintaining quality. Like agile, Ops are more about a culture of collaboration and agility than specific tools.
All Ops are a set of practices and cultural philosophies supporting the rapid delivery of services, applications, and projects by eliminating barriers between development and operations. Each one exhibits the following characteristics:
- Implement the utilization of agile principles and methodology
- Streamline & automate processes
- Enable businesses to scale IT efforts efficiently
- Focus on generating business insights and rapidly achieving business outcomes
- Focus on the journey, not just trying to buy a tool and be done
- Enable traditionally siloed teams to collaborate efficiently
- Increase time spent solving new problems, rather than maintaining
Having cleared up all that, we can move on to some major IT Ops.
DevOps is an outcome-driven culture, focused on agility, that aims to ensure quality in the development lifecycle. The role of DevOps is to bridge the gap between developer and infrastructure teams. Individuals from both teams work together to build this bridge when they communicate and converge around a common language. With faster communication, services are adopted sooner.
Learn more about how to design, implement and operate DevOps . Be sure to check out the case studies to learn more about what DevOps can do for you.
Due to the abundance of data available to companies, machine learning (ML), a subset of artificial intelligence, has become an essential tool for companies to gain insights and make predictions. As the use of ML models has blossomed, so has the need for standardized, streamlined, and scalable methods to manage them.
MLOps addresses this issue by incorporating DevOps principles and tools into the end-to-end ML process, alleviating many roadblocks teams face, and enabling teams to maintain and scale their models. By automating the retraining of ML models and tracking their status in real-time, MLOps greatly reduces the time data scientists spend on maintenance. This allows more time to be spent crafting new models. MLOps creates standard policies to which data and code must be upheld, allowing the data science, engineering, and production teams to have clear roles & responsibilities. This standard unlocks the full potential of the end-to-end ML production process since it reduces any potential information gaps between one stage in the process and another.
Learn more about if your organization is ready for MLOps, the value of MLOps and how to get started with MLOps for data scientists. Be sure to stay up to date with our ever-growing library of MLOps content.
In 2017 Gartner coined the term AIOps as the application of artificial intelligence (AI) to IT Operations. AIOps combines big data and machine learning to analyze increasing volumes, varieties, and velocity of IT data. AIOps increases visibility up and down the IT stack, reduces tactical troubleshooting and "war rooms," and uses predictive capabilities to identify issues before they become problems. All of this minimizes disruptions, secures data, improves end-user outcomes for happier customers, and reduces costs by tens or hundreds of millions of dollars.
AIOps provides insights from across IT operations in a consumable manner like a dashboard allowing IT Operators to visualize and understand the present state. Predictive capabilities are enabled by this deeper understanding of current trends and nascent problems. AIOps platforms enable AIOps to also draw from its roots of DevOps and utilize automation to self-heal.
An AIOps platform uses machine learning to review data from the IT ecosystem and determine normal levels of application performance. The platform then sends alerts when performance risks fall below an acceptable level and generates recommendations to address potential issues based on what was successful on similar issues in the past. This lowers Mean Time to Remediation (MTTR).
For businesses to maximize their potential for growth, it is essential to understand IT Ops at their core. Ops support the rapid delivery of services, applications and projects by eliminating barriers between development and operations. By applying Core Ops principles to your IT operations, your business can reap the benefits of agility, scalability and automation.