The Epic Guide to Artificial Intelligence for DevOps Automation
It seems that AI is everywhere nowadays – it is transforming many industries, including DevOps. Its strong presence has already impacted DevOps automation trends, and it continues to change the way DevOps operates and assists developers in their everyday tasks. Today we’ll talk about AI for automation in DevOps, and how businesses can benefit from it.
What is DevOps automation?
DevOps automation is what can serve as a bridge between “Dev” and “Ops” – it is a set of tools and technologies that help businesses get rid of repetitive and mundane tasks, as well as avoid human errors, speed up development and delivery processes, and do many other things. Automation is the key to higher productivity and better performance, and it is a great way to relieve developers of a ton of pressure and boost cross-team collaboration inside an organization. From cost-effectiveness to higher quality, there are tons of DevOps automation benefits that every business should take advantage of.
To experience an even better effect from automating various tasks, many companies use AI for DevOps Automation. And even though AI is still a relatively new technology, it is already breaking all the records, especially when it comes to automating all sorts of activities and projects.
To grow your business, you should consider all the trends and changes in the modern-day market. It is getting harder to adjust to our fast-paced world, so the best way to secure success is to start exploring the benefits of DevOps automation and make the most out of them. Businesses from all industries have already applied DevOps methodology to their operations, and those who haven’t yet will be soon left behind due to the inability to stay abreast of trends and new technologies.
Artificial intelligence and DevOps automation: What can be automated?
Developers can leverage AI to automate a huge variety of processes in DevOps. Let’s go over a few:
- Continuous integration and deployment
By applying AI/ML to CI/CD, such processes as building, testing, and deployment can be significantly improved. AI will perform such activities as analysis of code changes, automated testing and deployment pipelines, and many other tasks. AI will transform the CI/CD pipelines, making them more reliable and efficient.
AI-driven tools can help developers create monitoring and altering systems that would not only analyze large amounts of data, but also detect anomalies, predict possible bottlenecks and issues, enable troubleshooting, and do many other things that would help businesses avoid some common errors and prevent issues before they happen.
- Security & Compliance
The combination of AI and DevOps bolsters security and compliance, making applications and systems even more secure and protected than before. And thanks to DevOps automation, it is possible to enable one-click compliance reporting, create manageable systems, ensure governance, integrate security into literally all DevOps processes, and take action in many other areas of DevOps where security will now be at even higher levels.
- Performance optimization
Performance optimization requires a lot of time and resources to be spent on analysis of the already existing systems and determining what kind of bottlenecks there currently are. Instead of making this process manual, it’s best to use AI & DevOps practices to not only automate performance optimization but also explore various opportunities for ways to do it more effectively.
- Incident management
Such a big task as incident management can be a lot to handle even for a team. The best way to deal with it is to use AI which can boost the process of working with a ton of data points, which all require analysis and communication. By utilizing such tools as AI chatbots and virtual assistants, developers can automate such processes as incident reporting, resolution, and communication, making it easier to identify and solve the problem.
There are many things artificial intelligence is capable of, but what everyone who wants to make the most of the AI & DevOps combination should keep in mind is that AI enables seamless operations and multiples efficiency by at least 10, making DevOps one of the strongest technologies out there. And the best time to experience its power has already come!
What are the benefits of DevOps automation?
There are many advantages to DevOps and automation that allows businesses to optimize most of their processes and focus on their core competencies and goals. Let’s take a look at some of the biggest benefits of DevOps process automation:
- Fewer errors
As much as talented humans are, they are also capable of making many mistakes that machines won’t. By delegating some tasks and automating them, developers will be able to avoid a myriad of problems that potential errors could have caused. Moreover, thanks to DevOps automation, it is possible to predict some issues and errors that would otherwise cause a lot of trouble.
Scaling your project up and down is very hard to do manually. With DevOps, it is not a problem anymore. Automation and DevOps enable you to scale multiple projects and their deployment to various environments within a very short time, making it easier to adjust to changes and fix problems more quickly.
- Increased speed of delivery
When it comes to how much businesses can speed up such processes as code application, testing, and deployment, there is a ton of good news – thanks to DevOps automation, it is possible to make all these processes much faster. First of all, instead of relying on the team of developers to perform all these tasks manually, which can sometimes take a long time, you can automate them instead and they will happen without the need to wait for someone to get started. Also, automated tasks are often completed much faster than manually, because when a developer or engineer is working on a project, there are many things that need to be done, and it cannot happen simultaneously because of human nature. But with DevOps automation, everything will be completed in an instant.
Automated processes are less prone to errors, which means that they are more likely to stay consistent unless you decide to change something. This is very different from the manual approach since when it’s only humans who are working on the project, it becomes much harder to predict outcomes.
- Higher productivity
Thanks to DevOps automation, developers don’t have to spend their time on tasks that can often be very time-consuming and require a lot of effort. Instead, teams can focus on other priorities, which will result in double the amount of work completed within the same time limit.
Automation is one of the best ways to combat the competitive environment of today’s world. Automated DevOps pipelines free up a lot of space for further improvements and changes that might boost the performance of your team and completely revamp your business’s processes, making them well-optimized and more efficient.
What are the AI tools for DevOps automation?
There are many tools that are trusted by professional developers when it comes to DevOps infrastructure automation like Jenkins, Docker, Ansible, and many others. But what about AI-powered tools? Is there anything worthy available right now? Well, even though there aren’t many AI-driven instruments right now, here are some useful programs and platforms that might come in handy:
This is an ML observability platform for monitoring, troubleshooting, and fine-tuning your models. Thanks to this tool, developers can get a better understanding of whether their models are performing well, as expected, or if there is something wrong with them and what exactly the problem lies in.
This is a platform that strives to provide developers and engineers with Slim tools that would enable them to develop containers and push them into production. The platform offers a develop-first approach, which means that the needs of a developer are always kept in mind. Slim.ai can be run on already existing software – no additional code needed. With the help of this platform, devs can increase the security of their applications and remove vulnerabilities in no time.
Developers claim that Modular is a platform capable of helping teams develop, deploy, and innovate much faster. The platform offers an inference engine that is supposed to unify AI frameworks and hardware, which enables developers to deploy to any cloud or on-prem environment with almost no code changes.
The platform helps with the automation of insights, which enables rapid alerting, incident detection, correlation, and resolution. With the help of New Relic, developers can easily identify root causes and find solutions to problems with ease. The platform gives teams a chance to prioritize the work and focus on solving real issues, instead of responding to all alerts.
There are tons of other tools and programs available out there – and there are many more to come. Make sure you keep up to date with the latest news in the AI world, because tons of new apps appear on the market every day, and some of them are a real gem.
Use Case: How to apply AI and DevOps in Healthcare?
How is it possible to use AI combined with DevOps in the real world? Let’s take a look at an example of how to apply AI to solve modern-day problems:
Case: Patient Monitoring and Alert System
Objective: Improve patient monitoring and response time in healthcare facilities using DevOps automation and AI.
Here is how we can implement automation to streamline such processes as monitoring and management of patient monitoring systems:
- Continuous Integration and Deployment. It includes automation of such processes as building, testing, and deployment of patient monitoring software updates to ensure quick and efficient delivery of new features and bug fixes.
- Infrastructure Automation. After utilizing infrastructure-as-code tools to automate the provisioning and configuration of monitoring devices, sensors, and network infrastructure needed for patient monitoring, healthcare facilities will be able to save more time and resources, reducing manual labor.
- Continuous Monitoring. Implementation of automated monitoring systems that collect real-time data from patient monitors and other medical devices will enable proactive detection of anomalies and performance issues.
- Incident Response and Remediation. By setting up automated incident response workflows, healthcare institutions will be able to promptly address and resolve issues identified in patient monitoring systems, which also ensures minimal downtime and maximum availability.
When it comes to the AI part, here is how we can integrate it into patient monitoring systems:
- Real-time Data Analysis. By employing AI algorithms, healthcare facilities will get access to analysis of the continuous stream of patient data collected from monitoring devices, such as vital signs, ECG readings, and oxygen levels, and will be able to identify patterns, trends, and anomalies.
- Predictive Analytics. Application of machine learning models to predict potential deterioration or adverse events in patients based on their historical data and other contextual information.
- Intelligent Alerts: Develop AI-powered alert systems that prioritize and escalate critical patient events to healthcare providers, enabling them to respond promptly and effectively.
The utilization of DevOps automation and AI in the healthcare industry has numerous advantages, including the following:
- Patient safety will be greatly enhanced, enabling prompt detection and response to various critical events and situations.
- Efficiency will be improved thanks to simpler patient monitoring systems, which also reduces manual efforts.
- AI-driven predictive analytics enable proactive healthcare, which helps to address patient needs beforehand and prevent issues from occurring.
- Healthcare facilities will have more room for scalability and flexibility, which allows them to adjust to changing requirements and new technologies much faster.
The combination of DevOps automation and AI in healthcare leads to improved patient monitoring, response time, and overall quality of care, resulting in better patient outcomes.
The Future of AI and DevOps Automation
Many experts say that this is just the beginning of a real AI revolution. As we can see from the case of ChatGPT, the demand for AI is rapidly increasing, and more and more apps, programs, and platforms that are AI-driven are emerging and becoming popular. This trend will continue growing, and AI is likely to stick with us in the future. It will have a profound influence on the DevOps industry, which will change a little bit due to AI’s impact, and we will see its strong presence in many DevOps operations.
What is an example of automation in DevOps?
Developers can automate DevOps with AI in many different ways, including:
- Natural language understanding in DevOps
- Sentiment analysis for automation in DevOps
- Text classification and automation in DevOps
- Intelligent process automation in DevOps
- NLP-based automation in DevOps
- Voice recognition and automation in DevOps
- Data-driven automation in DevOps
- Predictive Analytics for automation in DevOps
Will DevOps be automated?
DevOps automation is inevitable – however, it doesn’t mean that all tasks will be automated and eventually, engineers and developers will be replaced by robots and AI. DevOps is mainly about improving communication and collaboration between teams, and utilizing various tools and techniques, including DevOps automation best practices, to enhance the production and delivery processes. AI will simply serve as a way to get rid of mundane and repetitive tasks, enabling professionals to focus on the most important parts of projects they’re working on, but it will never completely replace real experts.
Which language is best for DevOps automation?
There is no “best” language for DevOps automation, however, there are some languages that are most commonly used in the DevOps community:
The following languages can be most helpful when it comes to automating some tasks like deployment, configuration management automation, provisioning, etc.
What DevOps processes can be automated?
Automation is extremely important in DevOps, which means that many processes can be at least partially automated. Here are some of them:
- Infrastructure as code
- Containerization and orchestration
- Release management automation
- Agile DevOps automation
- DevOps toolchain automation
- Monitoring and alerting automation
- Security automation in DevOps
- CI/CD automation
It’s crucial to remember that even though many DevOps processes will be somehow influenced by automation, not all of them will or should be automated. The goal of automation is to enhance quality and productivity, and overdoing it might be harmful. Automation should be implemented strategically, with the company’s needs and goals in mind.