Top Ways Machine Learning Can Optimize DevOps

DevOps combines cultural philosophies, practices and tools to accelerate the software development process to deliver value to customers faster, without adverse impact on code quality. It enables enterprises to better serve their customers and stay ahead of the competition curve.

Traditional DevOps has come a long way over the past decades, enabling organizations to implement CI/CD pipeline. However, most of the engineering teams are still relying on manual and human-driven automation processes. While this process may still serve the purpose, the element of optimization is missing.

To combat this, DevOps automation is the answer. With rapidly growing Artificial Intelligence and machine learning, DevOps is evolving. Not just software development and testing, but even software release and deployment, AI is offering better alternatives in the form of DevOps automation.

Let’s check some of these exceptional examples here.

1. Automated Code Reviews

Code review is a technique that improves the quality of codebase. There are multiple developers involved in the process of bugs or other shortfalls in the code before passing on to someone else. Automated tools, as the name suggest are time-efficient and cost-effective alternate to manual code reviews.

Once a thing of discussion, Automated code review tools are now more accurate, effective, and customizable. While automated tools help you make the code more efficient with improved quality, developers can focus on more important tasks building their own applications, rather than reviewing and inventing around someone else’s code. Software development houses looking for faster feedback, improved code quality, and quick turnaround rely on automated code review.

2. Automated Code Analysis Tools

AI/ML-enabled smart code analysis tools can garner valuable data from the code repositories and learn from the same. These tools can analyze the code to recognize the intent and track the changes made by the developers. Based on this, they can offer valuable suggestions for each line of the code to improve quality.

While others take a different approach to analyze codes, AI-based code analysis makes the process more efficient. Having learnt from millions of code reviews, code suggestions enabled by AI/ML tools is more effective and quickly identifies the lines of code that impact the application performance. These tools are capable of recognizing and eliminating code issues such as resource leaks, wasted CPU cycles, potential concurrency race conditions, and more. You can also integrate these automated code analysis tools with CI/CD pipeline, in either code review stage or application performance monitoring state, or both. After successful code reviews, developers can also implement automated unit test creation and save around 20% of the developers’ time within a sprint.

3. Automated Use Test Creation

Growing implementation in intelligent automation, analytics and AI have enabled organizations to offer cost-effective software development experience and quick turnaround to their customers. While many businesses have already eliminated manual and scripted testing, many others are on their way to apply precision testing to accurately target the applications to be tested, and making smart resource allocation a priority. As enterprises implement successful automated testing, rapid advances are being made to make the testing truly touchless. Here are some of the trends driving the evolution:

  • AI-powered, real-time monitoring and integration that helps businesses switch to efficient monitoring and integration instead of issue-based resolution.
  • Apply AI to implement self-healing testing that monitors and recognizes the change in codes. This way tests updates can be automated to make the application more relevant and bug-free.
  • Using applied analytics, enterprises can switch from “test data” to “test insights” to seamlessly manage exponential growth in data volume.
  • Build interconnected virtual teams of today with best industry minds build around same business purpose regardless of industry, business or technology they come from.  

4. Era of Low Code or No-Code Platforms

Tools and skills to create robust automated test cases are scarce and expensive. AI and ML powered tools can generate automated tests with little to no code by learning from app flows, screens and elements. Low-code software development platforms provide software development environments for businesses to develop quickly with less coding. These profiles allow for base-level code, script, and integration so that companies can prototype, build, or scale applications without having to invest in complex infrastructure.

With low-code tools, enterprises can implement rapid application software development with custom workflows and functionality. They can quickly interpret information from databases or spreadsheets and create applications to access and manage information. These platforms can also self-heal between each test run, helping developers save time on software development and develop more innovative features.

 5. Process Automation for DevOps

To deploy automation, DevOps team need the right balance of RPA and manual learning. Human-in-loop automation can be implemented to DevOps platforms including processes like incident management, service requests, and continuous deployment and delivery. 

Instead of automating from end-to-end, DevOps teams use incremental automation to strengthen and build the system. The initial step is to codify free-form processes for the teams to identify predictable steps to be automated. Teams can, therefore, make advanced workflows, automating the processes and incorporating human checkpoints for areas that need manual interruption. As teams automate, they build more sustainable systems efficient to deliver seamless performance in complex environment.

6. AIOps

Digital transformation is the mix of DevOps and adoption of cloud computing along with advanced technologies like containers. It represents shift from centralized IT operations to applications and developers, rapid software development and innovation, acquisition of new users such as machine agents, IoT devices, APIs, and more.

These advances are straining traditional performance and service management strategies to breaking point, which is smartly handled by AIOps.

As modern application delivery and resilient IT infrastructure rely heavily on collaborative workflows and technical operations, environment around the codes is changing rapidly. It no more requires a large team of developers to continuously push the code. This is encouraging the IT teams and enterprises to adopt DevOps principles or continuous software development and deployment. AI ops specifically refers to the combination of AI, automation, machine learning and collaboration in DevOps. AIOps helps the technical teams to proactively detect problems in their applications and infrastructure, thereby, building the process for real-time incident response.

7. Utilize Trends Effectively

While regular analytics only focuses on past faults and issues, the shortfall is we can only take action once the issue has transformed into a bigger one.

Machine learning can garner insights from data and forecast issues. The algorithms can be trained to observe, analyze and identify patterns to predict these issues below the threshold and keep track of the trends that happen over time. This makes the output more actionable as it prevents the issues before they even happen thereby saving investments in time and resources.

The Bottom Line

DevOps and human engineering are here to stay. AI/ML can help optimize and accelerate tedious, error-prone software development phases. With proper analysis of the volumes of data, decision-makers can garner valuable insights and find successful ways for seamless integration of solutions to existing pr