By: Daneyal, Senior Product Engineer, Canvas

Two of the most pioneering IT technologies currently are artificial intelligence (AI) and machine learning (ML). They are significantly altering the ways of working in people’s personal and professional lives. DevOps, a critical foundation for successful software delivery, is a perfect candidate influenced by these cutting-edge technologies to perform tasks efficiently, improve performance, and aid in business growth. Enabled by AI insights, DevOps is taking a proactive approach by anticipating challenges before they occur by identifying potential bottlenecks, resource shortages, or performance issues in advance, ensuring smoother deployments. AI and ML are also aiding DevOps in intelligent automation, continuous testing, and monitoring, enhancing overall system reliability and efficiency.

Deep diving into DevOps and its automation

DevOps is a well-established set of practices centered on Continuous Integration (CI/CD) and infrastructure. DevOps tools are well-known for helping software development projects to move more quickly. An algorithm performs operations and procedures, allowing DevOps tools to execute effectively.

To automate DevOps, a company creates a custom AI/ML layer. The first step, however, is to establish a solid DevOps infrastructure. Once the infrastructure is in place, AI/ML is frequently used to improve efficiency. AI ML in DevOps allows teams to manage the quantity, speed, and variability of knowledge, allowing them to increase creativity and innovation. As a result, it improves automated enhancement and increases the efficiency of the DevOps team.

AI ML in DevOps

AI is the first tool for evaluating, computing, and making decisions in DevOps. AI transforms how DevOps teams develop, deliver, deploy, and organize applications to improve their performance and DevOps business operations.

Machine Learning is the use of AI and a combination of algorithms. ML is currently very popular in software products and applications. DevOps with automation enables a rapid SDLC and machine learning DevOps uniquely handles the quantity, velocity, and type of data generated by the next generation of automation.

Machine learning DevOps[i]—an effective union

• Training and testing a model can take hours or days because the AI/ML process depends on experimentation and model iteration. A distinct process is put in place to accommodate the timeframes and artifacts for a model development and test cycle. AI/ML model builds on time-sensitive applications.
• Teams are considering models for AI/ML that expect to deliver value over time rather than being built once. It has become a practice to adopt behaviors and procedures that allow for and plan for a model lifespan.
• DevOps is frequently defined as delivering a solution by combining business, development, release, and operational knowledge. Therefore, AI/ML is incorporated into feature teams along with assured discussion during design, development, and operational meetings.

Key points for applying AI ML in DevOps

• Increased implementation efficiency: AI reduces complexity by assessing and efficiently converting human intelligence. It’s believed that in the next 12 years, AI will make it possible for all professions to perform more effectively, especially those involving saving lives.
• Efficient use of resources: AI is responsible for automating routine and repeatable tasks, which reduces the complexity of resource management to some extent. It helps workers to concentrate on innovative solutions, challenging problems, and significant work. Chatbots are one illustration of such.
• Greater data accessibility: Access to all data may be critical for DevOps teams; AI collects data from multiple sources, making it reliable and useful for subsequent procedures.
• Production administration: Machine Learning DevOps aids in analyzing an application in production, particularly for larger data volumes and transactions. DevOps teams use ML to investigate broad patterns such as resource utilization, volume, etc.
• Troubleshooting analytics: ML now plays a significant role in analytics. In general, these tools process and identify threats. Other automation tools, such as ML, are also used to boost a ticket, alert operations, etc.
• Avoiding production setbacks: ML assists operations in avoiding problems and working faster with a quick response time. It comprises several algorithms, tools, and data sets that keep improving as time goes on. The AI platform machine improves at identifying patterns and employs them to create predictions as the input increases.
• Examining effects on business: ML systems can detect good and bad patterns by analyzing user metrics and are ready to assist developers in resolving bugs in applications.

Organizations help AI and ML to optimize DevOps

AI aids in the management of complex data pipelines and the creation of modules that aid in the application development process. In recent times, AI and machine learning have been spearheading digital transformation.

However, implementing AI and ML for DevOps poses several challenges for businesses of all sizes. A customized DevOps stack is required to capitalize on AI and ML technologies. If you have an open-source project, such as the Fabric for Deep Learning (FDL) or Model Asset Exchange, these tools can help the DevOps team. These technologies can aid in the efficient operation of the DevOps process.

By optimizing DevOps operations and making IT operations more responsive, AI and ML use results in true ROI for corporations. They are bringing humans and big data closer and boosting team productivity.

Conclusion

AI and machine learning are bridging the gap between humans and massive amounts of high-velocity data to obtain insights.  We can be more competent and efficient using AI and ML by learning and creating a system that can search, monitor, troubleshoot, or interact with data; basically, it can analyze user behavior in all aspects.

The future of DevOps is being realized today by a speedier and more effective SDLC, which is achieved by integrating AI ML in DevOps and producing a secure automated method. Organizations are taking this aggressive action to keep up with the quickening digital change. The anticipated new world won’t materialize if an organization keeps operating in the same manner and anticipates the same outcomes. The future of DevOps is now, and it is powered by AI and ML.

[i] The Role of Artificial Intelligence and Machine Learning in DevOps, Shraddha Suman, Teleperformance, February 2, 2024: https://www.teleperformance.com/en-us/insights-list/insightful-articles/global/the-role-of-artificial-intelligence-and-machine-learning-in-devops/