DevOps.com

  • Latest
    • Articles
    • Features
    • Most Read
    • News
    • News Releases
  • Topics
    • AI
    • Continuous Delivery
    • Continuous Testing
    • Cloud
    • Culture
    • DevSecOps
    • Enterprise DevOps
    • Leadership Suite
    • DevOps Practice
    • ROELBOB
    • DevOps Toolbox
    • IT as Code
  • Videos/Podcasts
    • DevOps Chats
    • DevOps Unbound
  • Webinars
    • Upcoming
    • On-Demand Webinars
  • Library
  • Events
    • Upcoming Events
    • On-Demand Events
  • Sponsored Communities
    • AWS Community Hub
    • CloudBees
    • IT as Code
    • Rocket on DevOps.com
    • Traceable on DevOps.com
    • Quali on DevOps.com
  • Related Sites
    • Techstrong Group
    • Container Journal
    • Security Boulevard
    • Techstrong Research
    • DevOps Chat
    • DevOps Dozen
    • DevOps TV
    • Digital Anarchist
  • Media Kit
  • About
  • AI
  • Cloud
  • Continuous Delivery
  • Continuous Testing
  • DevSecOps
  • Leadership Suite
  • Practices
  • ROELBOB
  • Low-Code/No-Code
  • IT as Code
  • More
    • Application Performance Management/Monitoring
    • Culture
    • Enterprise DevOps

Home » Blogs » Doin' DevOps » How Artificial Intelligence, Machine Learning Can Help DevOps

Artificial Intelligence, Machine Learning

How Artificial Intelligence, Machine Learning Can Help DevOps

By: Subramonian Krishna Sarma on July 18, 2018 2 Comments

Artificial intelligence (AI) and machine learning (ML) can help the humans in DevOps break free from focusing on simple activities. One aspect of DevOps is automating routine and repeatable actions, and AI and ML can perform these activities with enhanced efficiency to improve the performance of teams and business. There are algorithms that can perform many operations and procedures, allowing those in DevOps to execute their part effectively. This article discusses how DevOps engineers can use AI and ML to their benefit.

Recent Posts By Subramonian Krishna Sarma
  • Balancing UX and Privacy With IoT
  • Trends and Benefits of Serverless Computing
  • The Purpose of Microservice Architecture
More from Subramonian Krishna Sarma
Related Posts
  • How Artificial Intelligence, Machine Learning Can Help DevOps
  • The Role of AI, ML-Powered DevOps in Digital Transformation
  • Leveraging AI in DevOps for Non-Linear Scaleup
    Related Categories
  • AI
  • Blogs
  • Doin' DevOps
    Related Topics
  • ai
  • artificial intelligence
  • devops
  • machine learning
  • ml
Show more
Show less

Artificial Intelligence, Machine Learning Driving DevOps Evolution

Businesses are under a lot of pressure to meet customers’ ever-changing demands, and many embrace DevOps to improve their performance to some extent. However, it can be difficult for many companies to use AI and ML because of the complexity involved. To recognize any benefit with AI and DevOps, a creative mindset may be required.

DevOps Connect:DevSecOps @ RSAC 2022

The adoption curve of AI/ML may be relatively slow. Only 27 percent of CIOs surveyed by ServiceNow for its report, “The Global Point of View,” have hired employed who have skills in machine learning. But the fact is, DevOps experts may have a lot to gain by adopting even the most basic features of AI and ML. The same survey found that around 85 percent of C-level executives believe AI/ML can offer substantial value in terms of accuracy and rapidity of decision-making, which will lead to improved profitability for the company.

Tracking and organization in a DevOps environment requires effort because of the complexity involved in the distributed application, which traditionally made things difficult for the team to manage and resolve customer issues. Before the evolution of AI and ML, DevOps team members could spend hundreds of hours and a large amount of resources to identify one point within an exabyte of information. To solve such problems, the future of DevOps is AI-driven, helping to manage the immense capacity of data and computation in day-to-day operations. AI has the potential to become the primary tool for assessing, computing and decision-making procedures in DevOps.

AI’s Influence on DevOps

AI can change how DevOps teams develop, deliver, deploy and organize applications to improve the performance and perform the business operations of DevOps. There are three common ways through which AI may influence DevOps:

Enhanced Data Accessibility

The lack of unregulated accessibility to data is a critical concern for DevOps teams, which AI can address by releasing data from its formal storage—necessary for big data implementations. AI can collect data from multiple sources and prepare it for reliable and robust evaluation.

Greater Implementation Efficacy

AI contributes to self-governed systems, which allows teams to transition from a rules-based human management system. This helps address the complexity of assessing human agents to improve efficacy.

Effective Resources Use

AI gives much required competence to automate routine and repeatable tasks, which minimizes the complexity of managing resources to some extent.

How Can Companies Apply AI and ML to Optimize DevOps?

Organizations can apply AI and ML to greatly optimize their DevOps environment. For one, AI can help in managing complex data pipelines and create models that can feed data into app the app development process. By 2020, it’s expected AI and ML will take the lead in digital transformation, overtaking IoT.

However, implementing AI and ML for DevOps also presents a number of challenges for organizations of all sizes. To benefit from AI and ML technologies, a customized DevOps stack is required.

Open source projects such as the Fabric for Deep Learning (FfDL) and Model Asset eXchange (MAX) can lower the barrier of entry for companies, helping to implement machine learning and making the DevOps process more efficient.

Application of AI and ML can result in true ROI for a company by optimizing DevOps operations, making IT operations more responsive. They can improve efficiency as well as productivity of the team and play an important role in filling the gap between humans and big data.

Conclusion

A company that wants to automate the DevOps have to decide whether to buy or build a custom AI/ML layer. However, the first step is to establish a strong DevOps infrastructure. Once the foundation is created, AI/ML can be applied for increased efficiency. AI/ML can help DevOps teams focus on creativity and innovation by eliminating inefficiencies across the operational life cycle, enabling teams to manage the amount, speed and variability of data. This, in turn, can result in automated enhancement and an increase in DevOps team’s efficiency.

— Subramonian

Filed Under: AI, Blogs, Doin' DevOps Tagged With: ai, artificial intelligence, devops, machine learning, ml

Sponsored Content
Featured eBook
The State of the CI/CD/ARA Market: Convergence

The State of the CI/CD/ARA Market: Convergence

The entire CI/CD/ARA market has been in flux almost since its inception. No sooner did we find a solution to a given problem than a better idea came along. The level of change has been intensified by increasing use, which has driven changes to underlying tools. Changes in infrastructure, such ... Read More
« 7 Signs You’re Mastering Continuous Integration
Next-Generation Technologies Drive Widespread Adoption of Modern IT Service Delivery Solutions »

TechStrong TV – Live

Click full-screen to enable volume control
Watch latest episodes and shows

Upcoming Webinars

Automating the Observer: Lessons From 1,000+ Incidents
Thursday, June 30, 2022 - 1:00 pm EDT
Continuous Deployment
Monday, July 11, 2022 - 1:00 pm EDT
Using External Tables to Store and Query Data on MinIO With SQL Server 2022
Tuesday, July 12, 2022 - 11:00 am EDT

Latest from DevOps.com

Moving From Lift-and-Shift to Cloud-Native
June 30, 2022 | Alexander Gallagher
The Two Types of Code Vulnerabilities
June 30, 2022 | Casey Bisson
Common RDS Misconfigurations DevSecOps Teams Should Know
June 29, 2022 | Gad Rosenthal
Quick! Define DevSecOps: Let’s Call it Development Security
June 29, 2022 | Don Macvittie
Chip-to-Cloud IoT: A Step Toward Web3
June 28, 2022 | Nahla Davies

Get The Top Stories of the Week

  • View DevOps.com Privacy Policy
  • This field is for validation purposes and should be left unchanged.

Download Free eBook

DevOps: Mastering the Human Element
DevOps: Mastering the Human Element

Most Read on DevOps.com

Cloudflare Outage Outrage | Yet More FAA 5G Stupidity
June 23, 2022 | Richi Jennings
Developer’s Guide to Web Application Security
June 24, 2022 | Anas Baig
What Is User Acceptance Testing and Why Is it so Important?
June 27, 2022 | Ron Stefanski
Chip-to-Cloud IoT: A Step Toward Web3
June 28, 2022 | Nahla Davies
DevOps Connect: DevSecOps — Building a Modern Cybersecurity ...
June 27, 2022 | Veronica Haggar

On-Demand Webinars

DevOps.com Webinar ReplaysDevOps.com Webinar Replays
  • Home
  • About DevOps.com
  • Meet our Authors
  • Write for DevOps.com
  • Media Kit
  • Sponsor Info
  • Copyright
  • TOS
  • Privacy Policy

Powered by Techstrong Group, Inc.

© 2022 ·Techstrong Group, Inc.All rights reserved.