Artificial intelligence (AI) is rapidly advancing in every sector worldwide. Several industries have seemingly adopted and integrated artificial intelligence into their operations.
Those who are yet to do so are beginning to make room. Research published on DevOps.com predicts that by 2027, 80% of organisations will incorporate a DevOps platform into their toolchains.
This is because, yet again, AI has proven to be both practical and efficient in delivering tasks. It is capable of optimising tasks even in cloud computing.
As businesses continue to scale and demand more efficient cloud infrastructure, it has become increasingly inevitable for cloud engineers to gravitate toward more advanced and automated solutions, such as AI.
However, in recent times, even more exciting developments have emerged in this space, one of which is AI-native DevOps.
The traditional AI-assisted developments focus on integrating AI tools into the existing workflows of different organisations. But AI-native DevOps? It offers so much more. It’s a unique model that promises to revolutionise cloud operations.
Rather than patching operations with AI’s innovative features, AI-native suggests a more fundamental shift is needed to achieve better operational results. It emphasises the need to build software with AI as a core component of any organisation, from inception, essentially altering how applications are conceived, designed, and maintained.
Awesome, right? But how does this shape the future of cloud engineering? How does it work, such that it proves as effective as it sounds on paper?
How Ai-Native Devops Works
The standard function of DevOps is to bridge the gap between software development and IT operations. But AI-native DevOps doesn’t just bridge gaps; it embodies artificial intelligence completely. AI tools and machine learning are utilised as core components throughout every phase of the system, from development to operational flow.
This ultimately means that when AI-native DevOps is used, AI algorithms and automation don’t merely play a supportive or secondary role. Instead, they are a core, integral, and primary part of the system. This, in turn, influences how decisions are made within the system, predicts problems more effectively, and enhances the real-time utilisation of resources. It entirely relies on AI’s power to transform how cloud infrastructures are managed, tracked, and improved within the system.
And not only does this eventually make cloud engineering more efficient, but it also portrays it as a reliable and scalable process.
Over the years, cloud computing has been instrumental in transforming how businesses operate; however, managing the complexity that comes with voluminous cloud environments has consistently posed a problem.
From resource allocation to data security and troubleshooting, cloud engineers have faced and continue to face challenges. And while the traditional DevOps framework was revolutionary in its time, its limitations have become apparent as the world of cloud environments has evolved.
These shortcomings have therefore necessitated the development of AI-native DevOps, whereby AI is utilised to enhance every aspect of the cloud engineering lifecycle. This way, organisations are enabled to build systems that are not just reactive but also proactive and predictive.
Companies that have adopted this initiative have transformed their systems, leveraging some of the key impacts of AI-native DevOps, some of which are:
1. Better Automation and Incident Monitoring
It is vital to monitor actions in cloud environments. While conventional tools were used to generate large amounts of data, extracting valuable insights from this data without the aid of AI was a time-consuming task.
However, with AI-native DevOps, the system can automatically detect anomalies and potential issues before they escalate into big problems.
With AI-native DevOps, it becomes easier for monitoring tools to predict the outputs of systems with greater accuracy. It achieves this by leveraging historical data, system activities, and other external factors.
For example, Anomaly Detection Systems (ADS) can identify patterns that computer engineers may have missed or omitted in error, and then automatically suggest responses to resolve those identified issues, saving time.
2. Cloud Resources Optimisation
Cloud engineers consistently face the challenge of managing cloud resources, particularly as the number of services and applications increases. However, AI-native DevOps has offered a lasting solution to this by introducing advanced resource optimisation. Organisations can now use machine learning algorithms to ensure that cloud resources are allocated based on real-time demand and previous usage patterns.
This way, organisations prevent the overproduction of resources and therefore reduce operational costs. A perfect example is where AI models predict surges in usage of specific systems and then proactively optimise resources to avoid bottlenecks, resulting in both cost-effective and efficient resource management.
3. Strict Data Security and Regulatory Compliance
Organisations are constantly seeking ways to protect sensitive information. Security is, therefore, a critical concern for cloud engineers. However, AI-native DevOps has the potential to enhance data security by automatically detecting threats and flagging them for immediate attention.
This way, organisations won’t fall victim to unauthorised access and can better comply with data privacy regulations.
3. Quick Deployment Timelines
One of the most significant advantages that DevOps brings to cloud computing is its ability to streamline the software development and deployment process. But with AI-native DevOps, the deployment pipeline is even further accelerated. It automates functionality testing, quality assurance, and even code reviews before the systems are launched for use. It can automatically identify bugs and inefficiencies in code before it even reaches the production stage. This way, organisations reduce the need for manual intervention.
Additionally, machine learning can help organisations with Continuous Integration and Continuous Deployment (CI/CD) pipelines.
It achieves this through an accurate prediction of the potential success or failure of a deployment based on previous versions created, user feedback, and/or system behaviour.
This not only reduces the risk of failure or improves the overall efficiency of deployment cycles; it also saves time.
4. Brilliant Organisational Decision Making
What AI-native DevOps introduces is a new level of intelligence to help organisations make better decisions. Rather than relying on static rules or human intuition, which can be time-consuming, cloud engineers can now leverage AI’s ability to analyse vast datasets and offer insightful suggestions for organisations to improve their operations and workflows.
As demonstrated above, AI-native DevOps has undoubtedly provided excellent solutions to some cloud computing challenges.
However, it is not free of faults. Some of the challenges that organisations that adopt or are looking to integrate it into their system must navigate are detailed below:
1. Quality and Availability of Data
Artificial Intelligence relies mostly on large volumes of high-quality data to function effectively. As such, organisations need to feed it with accurate data. This means that they need to closely monitor their systems to generate quality and reliable data for AI-native DevOps to function at maximum capacity. Otherwise, it may struggle to achieve the desired outcomes.
2. Complexity of Integration
Organisations navigate a complex process to integrate AI tools into their existing DevOps workflows. Cloud engineers must therefore ensure that AI-driven systems work seamlessly with legacy infrastructure and tools, which may require significant adjustments to existing processes.
3. Talent Shortage
Another thing that organisations adopting AI-native DevOps should consider is the need for more specialised talents, such as cloud engineers, data analysts, or scientists. And as there’s an increase in the integration of AI into organisations, so have the demands for these professionals. Companies, therefore, need to be aware that to integrate AI-native DevOps successfully, they will be required to employ new sets of skills, and these are neither cheap nor easy to find.
4. Ethical Matters
Lastly, making matters that require deep decision-making, such as security and resource management, automatic can pose ethical concerns. Questions like: How transparent are AI-driven decisions? And how much control should human engineers retain? Then it becomes vital. Organisations that intend to transition must keep this in mind.
The Future of Cloud Engineering with AI-Native DevOps
AI-native DevOps continues to grow, and its evolution is likely to disrupt the field of cloud engineering. Shortly, automated, well-optimised, and intelligent cloud infrastructure may dominate the cloud engineering space.
And since AI systems will continue to manage most daily operational tasks, cloud engineers will gain the ability to focus on in-depth tasks and develop strategic initiatives.
Finally, in the long run, AI-native DevOps will lead the world to a new side of cloud engineering. It will offer more agility, scalability, and efficiency at its peak.
Integrating it into organisations’ systems will not only ensure more effective management of their infrastructure but also help them innovate faster and deliver better user experiences, those that give them a competitive advantage in their sectors.
As such, AI-native DevOps is more than a trend; it’s the next frontier in cloud engineering.
Meet The Writer
Inioluwa Shittu is a graduate of the University of South Wales, Cardiff, United Kingdom, and a certified AWS DevOps engineer. As a seasoned solutions architect with extensive knowledge of cloud computing strategies (IaaS), he leverages his technical expertise to impact the environment of building, deploying, and maintaining clouds.
She has extensive experience across various specialities, including Bash and Python scripting, with a focus on DevOps tools, CI/CD, and AWS cloud architecture, as well as hands-on engineering. Inioluwa continues to impact individuals, organisations, and the world at large with her tech-savviness.
