How AI Is Affecting Workflow For DevOps

  • By Miley Downing
  • 14-09-2018
  • Technology
artificial intelligence
Today, Artificial Intelligence – AI – is everywhere. Well, at least it seems that way. There’s no question that the technology holds quite a bit of promise because it enables all types of automation for business processes that are still done manually today. It also helps to deliver more power to data analytics.
However, one area where AI is really starting to shine is in the realm of DevOps. The fact is, this technology is helping DevOps teams automate processes, while increasing innovation.
Keep reading to learn more about the impact of AI on DevOps teams and how it’s going to continue evolving in the future.
What is AI?
Artificial Intelligence is the term given to a program that has been written for solving problems, which is often extremely difficult, that humans are already able to solve. The primary goal of many programmers and researchers in the field are to create programs that are able to arrive at a problems solution autonomously (without any supervision) by using logic or methods that may differ from what a traditional human may employ.
AI is touching a wide array of businesses and organizations that are now changing the way they operate. Including DevOps teams which is a significant reason to utilize this technology.
Why DevOps?
One area where AI is currently affecting and projected to have a huge impact is DevOps. According to experts, machine learning (ML) along with AI has the ability to enhance DevOps, even as more organizations are starting to adopt the model to help gain efficiencies in their application development, as well as other areas of their business. 
There are cultural, business and technical benefits that are realized when organizations adopt DevOps, which include:
- Increased communication and collaboration across various teams
- Automation
- Improved developer satisfaction related in more innovation and less time fixing bugs and fighting fires
- Faster development and “go to market” times
- Faster remediation for problems
- Reduced operational complexity
- Continuous delivery of fixes and features with fewer defects
While DevOps is focused on factors such as load testing, there are other tools besides AI that can help with this, such as seen in this source: AI comes in for other processes that are crucial to DevOps team. Keep reading to learn more.
The Influence of DevOps
AI can change the way DevOps teams create, deliver, organize and deploy applications to help improve the performance and to perform the business operations of DevOps. Currently, there are several ways that AI can influence, improve and change DevOps, which are highlighted here.
Enhanced Data Accessibility
A critical concern for any DevOps team is the lack of unregulated accessibility to data. This is something AI can help to address by releasing data from its formal storage, which is necessary for big data implementations. With AI, teams also have the ability to collect data from several different sources and prepare if for robust and reliable evaluation.
Increased Implementation Efficacy
AI actively contributes to all types of self-governed systems. This allows DevOps teams to make the transition from the rules based human management system. In the long run, this helps to address the complexity related to assessing human agents in an effort to improve efficacy.
Use of Resources Effectively
AI helps to provide competence to automate repeatable and routine tasks. This can help to minimize the complexity of managing various resources, at least to some extent.
use of resources effectively

Methods Companies can Use to Apply AI to Optimize their DevOps
Today, AI can be used by organizations and it can help to significantly improve their DevOps environment. One way this is possible is that AI helps to manage complex data pipelines to help create models that actually feed the data into app and the app development process. It is estimated that by 2020, AI will take the lead in regard to digital transformation – overtaking the IoT (Internet of Things).
However, the implementation of AI for DevOps also presents several challenges for businesses and organizations of all sizes. In order to benefit from AI technologies, organizations require a customized DevOps stack.
Various open source projects, such as the Model Asset eXchange (MAX) and Fabrice for Deep Learning (FfDL) can help to lower the barrier of entry for businesses, helping to implement the concept of machine learning and making the process of DevOps much more efficient.
The application of AI can provide a significant ROI (return on investment) for any company to optimize the DevOps operations, and working to make the IT operations much more responsive. They can help to improve the efficiency, in addition to team productivity and play a crucial role in filling the gap present between big data and humans.
The Bottom Line
Any company or organization that wants to help automate their DevOps needs to decide if they are going to buy, or custom build the AI layer. The initial step, however, is to establish a strong infrastructure for DevOps teams.
After the foundation has been created, AI is able to be applied for improved efficiency. With AI in place, a DevOps team can focus on innovation and creativity by eliminating various inefficiencies throughout the operational life cycle, which enables teams to actually manage the amount, variability and speed of data. As a result, this often results in automated enhancement, as well as an increase in the efficiency of a DevOps team and earlier completion times.

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Miley Downing

Miley Downing is the editor with who helps digital businesses reach their full online potential. She is passionate about programming and IT consulting. Her current focus is helping SaaS businesses create better world for our kids. She frequently writes about the latest advancements in the digital and tech industry.