Machine learning and Artificial intelligence technologies extend the capabilities that can be seen in daily lives like facial recognition, banking services, facial recognition, digital assistants, product recommendations, and more. The critical part here is not the technology but deploying them into the production model and keeping it operational.
While the software development teams and the AI/ML teams know how to develop different models to transform the business, things become tricky when it comes to implementing two technology. The process needs to be aligned to the development approach, which is DevOps.
According to Gartner reports, by 2023, 40% of the DevOps teams will be using monitoring apps to monitor applications and infrastructure.
DevOps has become the most important factor for cloud services as it emphasizes automation and brings a culture that encourages new work environments. DevOps is an application-centric change that focuses on the instrumentation, platform, and process to provide support to the applications.
DevOps is a set of practices that integrate the process of information technology and software development. DevOps helps build, test, and release software quickly. DevOps comprises four key principles that are listed below:
Going from an Agile approach to software development, a DevOps delivery process is based on the cross-functional approach of constructing and building applications quickly and in an interactive way. This technology can also helo you improve the flow and delivery of your applications by encouraging a more collaborative environment at other stages of the development cycle. Business models that position devops as a service provider enable startups and small businesses to implement the same technologies and processes for a competitive advantage.
With the companies focusing on being data-driven, Al and ML are experiencing huge growth in various folds in every field and are expected to expand. Businesses want to release their apps in the market instantly and, at the same time, want real insights on what’s trending and what’s not. This all can be done by DevOps!
DevOps involves all the agile practices by reducing the bottlenecks to deliver softwares and applications smoothly and quickly. But, the biggest hurdle for companies when it comes to this technology is scaling their CD/CI with a fragmented toolchain approach that restricts the scalability. Some companies even apply an ad-hoc DevOps toolchain approach that includes applying manual tasks and using irrelevant tools that don’t add any value to the business.
DevOps and AI are independent, ad AI is the technology that you can integrate into the system for enhanced functionality, and DevOps is a business-driven approach. Using the AI, DevOps teams can code, test, release and monitor software more efficiently. AI can also improve automation to identify, resolve, and collaborate between various teams.
AI also plays n important role in accelerating DevOps efficiency; it helps boost the performance by enabling the operation cycles and instant development to deliver a great user experience. Machine learning, on the other hand, will simplify the data collection process from various parts of the DevOps system. It includes defects, velocity and burn rate; data gathered from the continuous integration and deployment of tools is also part of DevOps.
Improved Data Access:
Unrestricted access to the data is one of the most important yet critical issues faced by DevOps teams. And AI will resolve this issue; it helps liberate that data from its organizational silos for collecting heaps of data. AI can also collect data from different sources and organize it for consistent and repeatable analysis.
Software Testing:
AI is an asset to DevOps; it helps in the software development process and makes testing more efficient. A huge amount of data produces through regression testing, user acceptance testing and functional testing. AI can decipher the pattern in the data collected by producing the output and identifying the regular coding practices that are responsible for different errors.
Timely Alerts:
Sometimes alerts come in huge numbers, and all are at high priority. This makes it difficult for the teams to respond and react, which is important for the DevOps teams. AI and ML help teams value their responses by prioritizing things on factors like their past behaviour, the intensity of alerts, the source of alerts and more.
High Execution Efficiency:
Artificial intelligence is changing the technology from the human-management, rule-based system to self-governed systems. This is not required because of the complexity of analysis agents can get, but also to enable the level of change, which is not possible.
Smart Resource Management:
AI gives the much-needed capability to help automate routine and repetitive tasks. With the evolution
of the AI and ML, the scope and complexity of the tasks get automated, and the employees will be able to focus more on creativity and innovation.
Faster Root Cause Analysis:
AI utilizes the patterns between activity and causes to identify the cause behind the failure. Most people don’t investigate failures; they analyze and resolve the issue and avoid the root cause analysis. Therefore, it is important to fix the issue permanently by analyzing the root cause. Therefore, AI plays an important role in identifying the root cause and resolving the issue.
Anomaly Detection:
As security is one of the most successful software implementations, DevSecOps is the most important aspect of software development. As a result, businesses should protect their security system as there’s an increase in DDoS attacks and security breaching. Therefore, AI can be used to increase the DevSecOps and enhance security by running an ML-based anomy through the central logging architecture. Combining DevOps and AI will prevent attacks from hackers and DDoS and ensure high performance.
Irrespective of the technologies and the concerns over DevOps, businesses should understand the importance of the workspace. Further, businesses can do wonders when it comes to offering great user experience and implementing things that not only increase ROP but also improve the work culture and efficiency of employees.
