7 Ways AI is Transforming DevOps

DevOps, a tech-savvy blend of Development and Operations, is a market expanding at stellar speeds today. In research conducted by Global Market Insights, the DevOps sector has emerged to be growing at a 20% CAGR (2020-2026).

Considering the rapid technological advancement in the context of Cloud, AI/ML, and DaaS,  it is not surprising that Gartner predicts 40% of DevOps teams to be preferring AI-based IT platforms by the time 2023 hits.

Qualitatively speaking, AI has a lot to deliver to the IT and DevOps spheres towards the improvement of process efficiencies and consolidating operations across enterprise incumbents. Let’s now see how AI is transforming the DevOps universe.

A portmanteau that combines not just Development and Operations, but also the tools, best practices and philosophies that an organization mobilizes to speedily deliver services and applications to the market, is called DevOps.

A stark contrast emerges when comparing the quality, speed, and quantity of the end-products delivered using traditional infrastructural and software development setups vis-à-vis DevOps methodology. This happens majorly because DevOps processes flow over bridges between various aspects related to applications and services; traditional methods are typically well-defined/frozen and quite siloed.

Automation plays a pivotal role in DevOps processes, and this helps remove the time-consuming manual tasking while also reducing errors.

A DevOps engineer is the orchestrator of the processes in the software development ecosystem from start to finish. This professional is a multitasker, handling collaboration between teams, quality assurance, code release management, and many other events. Let’s look at what roles a DevOps Engineer plays.

It is the job of the DevOps engineer to keep the lifecycle of software development balanced by providing strategies, methodologies, tools, practices, or processes needed to tackle the situation. The DevOps engineer induces a culture of seamless coordination between all the professionals involved, in order to ensure smooth development even during Agile sprints. It falls under the ambit of a DevOps engineer to combine code and manage applications

Artificial intelligence has the capability to draw out the latent efficiencies that often get lost amidst manual tasking. The seven major ways that AI can make DevOps better are discussed below.

DevOps productivity is gained by leveraging the code autosuggest capability of AI engines reinforced with machine learning. The engines respond relatively quicker each time, when prompted for code requests – a feature that has gained much favor with DevOps professionals

Projects can be run on tight timelines and still be kept on schedule using AI-based requirements management. NLP is helping streamline the Requirements Management Documents to allow pushing out updates based on what users wish to see in the new version of an application or software. Deloitte reports that organizations have experienced a 50% reduction in the time taken to review user requirements by implementing AI solutions for DevOps

Debugging has been made simpler and less stressful with the help of AI in DevOps. Facebook reports that its AI-based bug-finder returns correct results 80% of the time. The entire codebase can be scanned by AI implements to spot vulnerabilities or logical variants; additionally, the tools are also capable of suggesting a code fix automatically

AI in DevOps is helping save valuable time by automating the quality assurance aspect. By using unique codebase attributes to vet application quality, and by creating and running tests automatically, a huge amount of developers’ time can be saved. Additionally, AI for DevOps is proving to be effective in preventing the rollout of defective apps or software in the consumer sphere by performing root cause analysis and troubleshooting, according to Deloitte

Machine Learning, as an implement, has the capability of establishing benchmark coding activity for the purpose of vulnerability and anomaly detection in real-time. The engine compares ongoing coding activity based on established benchmarks to flag anomalies or variations in real-time

In a data-driven world, AI has the potential to help DevOps glean insights on its own processes for application performance monitoring and quality assurance. It is possible to understand how software development cycles perform for different projects and collate the insights for comparative analysis, with a view to screen best practices from the wasteful ones

Remote-style of working across geographical locations posed a challenge for the DevOps culture; AI is helping to stitch it back together. Improved collaboration through integrating data and workflows on the same platform makes it possible to spot gaps in processes – improving the traceability in the complete software development cycle

DevOps is already a highly evolved methodology of software development that is well-rounded and highly function-oriented. Augmented with the power of artificial intelligence and machine learning, it can go further still in imbuing efficiency and accuracy in the entire ecosystem of software development.