DEVOPSdigest

DEVOPSdigest asked the top minds in the industry what they think AIOps can do for DevOps and developers. Part 5 covers testing and quality.

Start with What Can AIOps Do For DevOps? - Part 1

Start with What Can AIOps Do For DevOps? - Part 2

Start with What Can AIOps Do For DevOps? - Part 3

Start with What Can AIOps Do For DevOps? - Part 4

TEST-DRIVEN APPROACH

DevOps teams can leverage AIOps to proactively manage deployments with a test-driven approach that reduces mean time to recover and restore (MTTR). With AIOPs, analytics are leveraged to prepare, deploy, test, and remediate with intelligent automation pipelines that lead to better outcomes, cost savings, and enhanced observability.
Randy Randhawa
SVP of Engineering, Virtana

SIMULATING REAL WORLD SCENARIOS

AIOPs ,when implemented correctly, can help developers simulate real world scenarios and pinpoint potential issues in applications so that they can be fixed preventatively. This could be a huge competitive advantage for companies who are relying on software to build immersive and memorable experiences for customers.
Milan Bhatt
EVP, Hexaware

CI/CD PIPELINE

AIOps can be leveraged to monitor, correlate, and recommend actions for continuous integration/delivery (CI/CD) pipeline like it is used for assuring application performance and health after deployment.
Randy Randhawa
SVP of Engineering, Virtana

UNDERSTANDING IMPACT OF CHANGE

AIOps could bring developers' agility with confidence in product development and quality delivery by helping developers understand impact of change across teams and distributed architecture to operate with each other without worrying about customer impact.
Bhanu Singh
VP Product Development and Cloud Operations, OpsRamp

SHIFT LEFT QUALITY ASSURANCE

AI in DevOps cycle enables to shift-left the quality assurance in a more guided and automated way. Instead of finding problems in production, you can find them, or do right, while developing.
Antonio Alegria
Head of AI, OutSystems

AIOps can help flag non-compliance at an early stage of the development.
Muraleedharan Vijayakumar
Senior Technical Manager, GAVS Technologies

DEVELOPING ALGORITHMS

Developers and AI/ML scientists will continue to evolve in their specialties. Developers and scientists need to be focused on designing the right metrics, and researching and developing algorithms to deliver to those metrics. The AIOps team should be in a critical partnership with the algorithm developers to ensure that the data pipelines are constantly delivering that technology and that the environments are scaled in a way to keep the data-pipeline, model-generation machine running smoothly.
Michael Estrem
Senior Director of Data Science and Analytics, Lucidworks

FUTURE PROOFING APPLICATIONS

AI ensures teams are better equipped to manage application dependencies and ensure that regardless of what changes are made, applications never break and are able to seamlessly adapt to inevitable change. Instead of finding problems in production, you can find them, or do right, while developing.
Antonio Alegria
Head of AI, OutSystems

Go to: What Can AIOps Do For DevOps? - Part 6