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 » Continuous Delivery » XebiaLabs Injects Predictive Analytics Into DevOps Platform

XebiaLabs Injects Predictive Analytics Into DevOps Platform

By: Mike Vizard on April 17, 2019 1 Comment

XebiaLabs is moving to take the guesswork out of DevOps following the launch of a set of tools that leverage machine learning algorithms to predict when and how the release of an application is most likely to go awry.

Recent Posts By Mike Vizard
  • TechStrongCon: Time to Build an Army of Citizen Developers
  • Buildkite Adds Analytics Tools to Identify Flaky App Tests
  • Survey Reveals High Cost of Application Modernization
More from Mike Vizard
Related Posts
  • XebiaLabs Injects Predictive Analytics Into DevOps Platform
  • GitLab Gets an Overhaul
  • XEBIALABS ANNOUNCES RECORD GROWTH AS ENTERPRISE DEVOPS AND CONTINUOUS DELIVERY ADOPTION ACCELERATES
    Related Categories
  • AI
  • Continuous Delivery
  • DevOps Toolbox
  • Doin' DevOps
    Related Topics
  • application release
  • application release lifecycle management
  • devops
  • machine learning
  • Predictive Analytics
Show more
Show less

The Risk Prediction Module being added to the XebiaLabs DevOps platform comes on the heels of XebiaLabs’ move to make its XL JetPack software for automating application deployments available on the Amazon Web Services (AWS) Marketplace. XL JetPack is an automation framework based on YAML files and a declarative programming framework that enable DevOps teams to push applications into production on a cloud platform in as little as 15 minutes.

DevOps Connect:DevSecOps @ RSAC 2022

XebiaLabs CEO Derek Langone said that while DevOps advancements have been made in terms of achieving continuous integration, the goal of achieving continuous delivery has proven to be more elusive. As automation frameworks such as XL JetPack become more accessible, however, IT operations teams will be better able to keep pace with the rate at which organizations want to be to deploy and update applications, he said.

It’s becoming increasingly apparent that a combination of automation frameworks and machine learning algorithms will enable DevOps teams to make a significant leap forward in terms of application release lifecycle management. Because of all the nuances of the different platforms that IT organizations are trying to leverage, it’s become a major challenge to roll out an application manually. As the number of applications that need to be released at roughly the same time increases, managing the release cycle process is moving beyond the capabilities of an IT team made up of mere mortals.

The Risk Prediction Model from XebiaLabs promises to increase the odds of success by applying predictive analytics infused with machine learning and other proprietary XebiaLabs algorithms to identify DevOps bottlenecks before they are encountered. Capabilities provided by that module include alerts that warn the team when a release is likely to be delayed or to fail before the release pipeline starts running; a Risk Forecast view that summarizes predicted delays and failures for every task in the release process; an overview of statistics for similar releases to provide historical context; and forensics tools that uncover flaky automated tests, problematic build setups, long-running deployments and time-consuming manual tasks. That Risk Prediction Module is meant to complement the risk scoring capability that XebiaLabs already includes in its platform.

Arguably, the biggest barrier to DevOps adoption today continues to be the level of expertise required to succeed. But as the combination of predictive analytics infused with machine learning algorithms and algorithms that can discern instantly how changes to the IT environment will impact applications become more common, the level of expertise required to master DevOps processes should decline. As that transition occurs, the number of organizations embracing DevOps more broadly should increase.

In the meantime, DevOps teams would be well-advised to consider to what degree they might next want to rely on predictive analytics to prescriptively automate processes with eye toward eliminating the need for as much human intervention as possible.

— Mike Vizard

Filed Under: AI, Continuous Delivery, DevOps Toolbox, Doin' DevOps Tagged With: application release, application release lifecycle management, devops, machine learning, Predictive Analytics

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
« Building Amazing Apps, Part 3: Optimizing the Network
HybridOps: Driving Better Collaboration and Productivity in IT »

TechStrong TV – Live

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

Upcoming Webinars

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
Goldilocks and the 3 Levels of Cardinality: Getting it Just Right
Tuesday, July 12, 2022 - 1:00 pm EDT

Latest from DevOps.com

Rust in Linux 5.20 | Deepfake Hiring Fraud | IBM WFH ‘New Normal’
June 30, 2022 | Richi Jennings
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

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

The Automated Enterprise
The Automated Enterprise

Most Read on DevOps.com

What Is User Acceptance Testing and Why Is it so Important?
June 27, 2022 | Ron Stefanski
Rust in Linux 5.20 | Deepfake Hiring Fraud | IBM WFH ‘New No...
June 30, 2022 | Richi Jennings
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
The Two Types of Code Vulnerabilities
June 30, 2022 | Casey Bisson

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.