Looking for a way to reduce the growing complexities of your DevOps workflows? Given the various challenges of Agile DevOps, software developers expect a solution that can bring automation strategies to the SDLC. Artificial Intelligence (AI) in DevOps is the latest invention that aims to level up DevOps strategies to drive automation.
“AI-driven DevOps automation strategies accelerate the SDLC phases with improved software quality and performance.”
How is AI molding today’s DevOps processes? And what’s the next course of action for today’s DevOps leaders? This epic guide explains how DevOps engineers can leverage AI capabilities for efficient software development. So let’s get started!
What Are The Advantages Of Using AI In DevOps?
AI technologies like Machine Learning have a significant impact on accelerating positive DevOps. Here is how AI-powered solutions can benefit the DevOps teams:
Increased Performance Efficiency
Developers and System Engineers can increase their operational efficiency with AI-power. It helps them incorporate several automation workflows instead of manual efforts that are associated with software development.
Improved Development Accuracy
Accuracy and consistency are crucial in software development. AI/ML technologies reduce manual efforts and eliminate the chances of human error. It helps improve the process accuracy and achieve the desired outcomes.
Consistent Resource Optimization
With the help of AI, organizations can improve resource utilization in their DevOps culture. As they manage their resources consistently, they can optimize the process of resource allocation and decommissioning with automation.
How To Leverage Artificial Intelligence For DevOps Automation?
AI-driven DevOps automation is a modern approach that can help organizations produce quality software with reduced efforts. While Agile principles remain the core of DevOps, AI-powered tools enable DevOps automation in various stages of SDLC. Let’s review how to implement AI/ML:
#1 Automate Infrastructure Management with IaC
Under IaC, infrastructure isn’t a physical environment. Instead, it embodies the idea of software development using infrastructure as source code. However, it’s troublesome to manage extensive infrastructure with endless components. With AI, DevOps systems can achieve continuous delivery for infrastructure:
- Create guidelines for orchestration tools like Jenkins to record data.
- Use a version-controlled repository to create separate and defined orchestrators.
- Plan to scale different infrastructure environments over time.
- Use an automation server to limit triggers and sync with the repository.
- Code and deploy verification processes and preferred actions.
AI enables developers to use uniform IaC processes when creating the pipeline and validating the trigger actions. This framework forms the right Agile environment where infrastructure resources remain flexible to scale.
#2 Using AI for CI/CD
Like IaC, another common way to use Artificial Intelligence in DevOps is for its CI/CD pipelines and Continuous Delivery workflows. Using AI-driven tools, developers can automate software code deployment without human intervention. So whenever someone modifies the source code, the tools auto-test the codebase before its integration and deployment. This process can reduce the risk of errors and improve the code quality.
#3 Automate Testing with AI
There is no need to perform manual testing any longer! AI-powered tools like Selenium can now automate the end-to-end testing process of software code development and deployment. AI perfectly analyzes what is changing and how it can impact future system performance. From acceptance testing to functional tests, DevOps consulting companies focus more on executing automated test cases than manual testing. Thus, developers can quickly fix potential performance issues without impacting production.
#4 Autosuggest Code Segments
Developers can write code faster using code auto-suggestions from AI/ML models. These systems use Deep Learning methods to autosuggest real-time code snippets that match the software’s development requirements. The AI-produced code is far better in accuracy than human-written code segments. Hence, DevOps development tools must integrate more supervised ML algorithms to auto-learn and respond to code requests.
#5 Automated Continuous Improvement in DevOps
Most of today’s DevOps teams struggle to refine product backlogs and create an intelligence automation system. However, AI incorporates these capabilities to drive Continuous improvement in the DevOps process:
- Doing code reviews to detect errors.
- Scoring software packages.
- Detecting codes that impact builds.
- Simulating production environments.
#6 AI in Quality Assurance and Control
QA/QC is a critical factor in estimating DevOps success. AI in QA/QC aims to bring automation to measuring software performance and quality. AI-based systems can perform Root Cause Analysis to discover the bottlenecks in the development process. It helps the developers identify the problem’s underlying cause and write more secure code in the future.
Case Studies: How Companies Are Benefiting From Using AI In DevOps?
As per Gartner, 68% of surveyed organizations aim to incorporate AI in their DevOps processes due to its potential to bring automation and extract data insights. Many organizations have already adopted Artificial Intelligence to improve their DevOps culture.
#1 The Transformation Of William Hill
The betting and gaming company turned to AIOps to enable real-time user monitoring. AIOps helped the company automate several processes by integrating diverse data sources and reducing system dependencies. AIOps also enhanced problem-solving speed by reducing alert volumes and uncovering correlations between alerts.
#2 TDC NetDesign: Achieve Proactive Problem Solving
Traditional IT problem-solving can bring frequent service disruption with downtime. AIOps uses dynamic baselines to predict and alert anomalies. TDC NetDesign used the same AI capabilities to predict hardware faults before they happen and automated the issue resolution process.
#3 Enablis: Boosting Efficiency With Automated Monitoring
DevOps firms often face monitoring tool limitations that impact their client support services. AIOps solved this problem of Enablis, a communication services provider, as they chose ScienceLogic for scalable monitoring. It helped them save costs, leading to 35% annual revenue growth through enhanced efficiency.
Best Practices For Implementing AI In DevOps Automation
Organizations can improve their DevOps automation processes using the following best practices to implement AI techniques:
Start Small and Iterate
It’s best to start small and then iterate. Identify specific areas in the SDLC where AI benefits the most. Gradually incorporate the right tools to replace manual processes with automation in DevOps systems.
Ensure Security and Data Quality
Ensure that the data remains high quality with ultimate protection. Comply with the workflows based on the data governance policies. Utilize secure data storage solutions for end-to-end data encryption.
Maintain Transparency
When using AI, update all stakeholders about which tools you plan to implement alongside DevOps CI/CD pipelines and workflows. Establish a clear product vision with dedicated lines of responsibility among the teams. It helps to maintain trust and transparency in the system.
Continuously Evaluate and Improve
With the changing requirements, evaluating the system’s performance is essential. As the workflows deliver proper outcomes, it becomes easier to maintain consistency.
What’s Next? Predicting The Future Of AIOps
The new norm of DevOps is here! AIOps is the latest practice, the perfect blend of Artificial Intelligence and DevOps. The main aim of AIOps is to drive automated development cycles with high-quality code. It reduces manual efforts by automating several stages of SDLC, like deployment and testing. As it accelerates every phase of SDLC, developers receive code suggestions to maintain QA/QC of the software. While many organizations have already started using AIOPs, it’s time to drive the change. So begin the DevOps transformation with AI/ML today!
FAQs
#1 What is AIOps?
The word “AIOps” is the combination of AI and IT Operations. This unique approach combines AI/ML technology with traditional IT operations. The ultimate goal is to automate various IT management and support processes. It follows DevOps principles to improve workflow efficiency and systems performance.
#2 How can AI improve DevOps productivity?
Artificial Intelligence can increase the productivity of DevOps practices by creating error-free codes and automated testing during deployment. AI can also automate code integration processes using CI/CD pipelines. As teams can track the process in real-time with continuous monitoring, it increases the system’s productivity.
#3 What are the latest trends in AI for DevOps?
Most organizations of 2023 are following the below trends of AI in DevOps:
- Transform towards Continuous Integration and Continuous Delivery
- Usage of Data Science for analytics
- Developing applications in containers
- Adopting IaC for infrastructure deployment automation
- DevSecOps to improve workflow security
#4 Can AI Help automate code development in DevOps?
Yes, AI-powered tools can auto-suggest code segments to complete software development faster. These tools can write code in any programming language. Plus, they can detect code defects and auto-correct the missing code syntaxes. Hence, it can automate the code development in DevOps.
#5 How is AI impacting DevOps in 2023?
AI-powered solutions will soon replace other solutions for integrating and analyzing data with automation strategies in DevOps. By 2023, 40% of organizations will start using various AI-powered tools in the DevOps workflows. DevOps consulting companies like Algoworks are helping organizations adapt AIOps and transform how teams develop and deliver applications.
BDCC
Latest posts by BDCC (see all)
- DataOps vs DevOps: Everything You Need To Know - April 23, 2024
- Crafting Your DevOps Testing Strategy: Discover Top Tools & Best Practices - April 18, 2024
- Introducing AISecOps: Reshaping AI & ML Security Utilizing DevSecOps - April 12, 2024