The integration of artificial intelligence (AI) and machine learning (ML) promises to massively augment DevOps capabilities and transform how software teams build, test, release and monitor applications. As AI and ML advance, these technologies are poised to impact nearly every phase of the DevOps lifecycle. In this post, we’ll explore some of the key ways AI will reshape the world of DevOps in the years ahead.

Smarter Test Automation

AI will make test automation far more intelligent and efficient. Traditional test automation relies on scripted tests which are brittle and miss edge cases. AI will change this in a few ways:

• AI-powered test generators can dynamically create better test cases rather than rely on humans to manually code scripts. This results in greater test coverage.

• Intelligent test optimisers can continuously improve test suites by analysing test results and refinement. This maximises testing value.

• Self-healing tests can detect when scripts fail or need maintenance, automatically fixing or flagging them for humans. This reduces test maintenance costs.

• AI test analytics provide greater insight into the quality of test suites, identifying gaps in coverage and areas needing improvement.

Together, these capabilities will enable teams to achieve far higher test automation rates with greater effectiveness. Testing will evolve from rote manual scripting to intelligent auto-generation and optimisation.

Smarter Infrastructure Provisioning 

AI will also optimise infrastructure provisioning and management via:

• Predictive auto-scaling that can forecast load demands and cost-efficiently spin infrastructure up or down just ahead of time. This prevents over-provisioning while still meeting capacity needs.

• Infrastructure optimisation that tunes configuration settings in real-time for optimal performance and cost, avoiding manual tuning.

• Anomaly detection that quickly spots any infrastructure issues or outages through constant log and metrics analysis, enabling fast remediation.

• Automated remediation that can proactively fix many infrastructure issues without human intervention based on predefined playbooks and runbooks.

Together, these capabilities will enable “self-maintaining” infrastructure that efficiently scales, tunes, and heals itself as needed.