Technology advancements have taken the production abilities of firms to different heights across almost every industry.
Days are gone when we used to see only human-intensive tasks!
Now, the world is highly dependent on technology-driven systems that ease industry processes, from developing a product to releasing it to the market and further towards offering an unforgettable experience to end-users.
DevOps is one technology solution most commonly used in today’s tech world. It is especially effective for enhancing collaboration among teams and offering faster execution with less failure and a high recovery rate.
For the IT industry, DevOps appeared as a solution for advanced abilities such as continuous integration, continuous delivery, and faster innovation rates, among others, that can fasten the software process chain. That’s not the end!
Two of today’s most advanced technologies are the ones every other technology or industry wants to scale up their performance and productivity. While some of the leading market players are already using them, many medium and small companies are still using them.
They are undoubtedly Artificial Intelligence (AI) and Machine Learning (ML)!
It’s no surprise that any AI-powered, ML-capable system holds high regard in today’s smart world!
Applying static tools for deployments, provisioning, and application performance management (APM) has already seen its full potential and is becoming absorbed by the ever-growing industry demands.
The next quest has already begun for creative management tools that can apply intelligence to simplify the tasks of development and testing engineers. Here is where AI and ML play vital roles!
This article will show how AI and ML integrations can power DevOps. In brief, AI and ML help DevOps by automating routine and repeatable tasks, offering enhanced efficiency, and minimizing teams’ time spent on a process. Let’s look into more details!
Applying Artificial Intelligence (AI) to DevOps
The data revolution is one key aspect posing severe challenges to the DevOps environment.
Scanning huge volumes of data to find a critical issue in day-to-day computing operations is time-consuming and human-intensive.
That’s where Artificial Intelligence has its role in computing: analyzing and making an immediate decision that a human might take hours to make.
With the evolution of DevOps, two different teams started collaborating on a single platform, which requires effective tools that can reduce the occurrence of errors and revisit problems.
AI can transform the DevOps environment in various ways, such as:
- Data Accessibility: AI can increase the scope of data access for teams that typically face issues such as a lack of freely available data. AI enhances the teams’ ability to gain access to huge volumes of online data beyond organizational limits for big data aggregation. It helps teams have well-organized data scanned from widely available datasets for consistent and repeated analysis.
- Self-governed Systems: Adaptation to change is one fundamental limitation many firms have faced due to a lack of proper analytics that limits themselves to certain borders. AI has changed the scenario, bringing about a transition in analysis from being human to self-governed. Now, self-governed tools can drive many operations that humans might not be able to do quickly.
- Resource Management: Enhancing the scope of creating automated environments that run many routine and repeatable tasks, AI transformed the resource management process, opening more avenues for innovation and creating new strategies.
- Application Development: AI’s ability to automate many business processes and empower data analytics is likely to have a bigger impact on the DevOps environment. Many firms have already begun adopting AI and Machine Learning to achieve efficiency in application development.
AI can help your teams precisely identify the solution to your problem from a dataset instead of spending hours together on huge data volumes. This saves time and minimizes almost half of the work required.
Applying Machine Learning (ML) to DevOps
Machine Learning refers to the application of AI in the form of programs or data sets within machines in the form of programs or algorithms.
Habituating systems to automated learning capabilities speaks to the effective implementation of ML capabilities, making it culture the ‘practice of continuous learning’.
This makes it easy for the teams to deal with complex aspects such as linear patterns, massive datasets, query refining, and continuous insight identification at the speed of their executing platform.
As part of the process chain, ML helps in the easy fixing of bugs and also plays a vital role in making frequent modifications to the overall code hassle-free.
Following are key areas where ML integration means a lot for DevOps:
- Application Progress: While DevOps tools such as ‘Git’, and Ansible, among others, provide the visibility of the delivery process, applying ML to them addresses the irregularities around code volumes, long build time, delays in code check-ins, slow-release rate, improper resourcing and process slowdown, among others.
- Quality Check: After analyzing testing outputs thoroughly, ML efficiently reviews Quality Assessment results and builds a test pattern library based on discovery. This keeps comprehensive testing alive for every release, thus enhancing the quality of applications delivered.
- Securing Application Delivery: Securing application delivery is one key advantage ML integration offers DevOps. With ML in place, it’s easy to identify user behavior patterns, thus avoiding anomalies in the delivery chain. These might lead to access to anomalous patterns in key areas such as system provision, automation routines, repositories, deployment activity, and test execution, among others. Stealing intellectual property and including unauthorized code in the process chain are among the most common deviating/bad patterns.
- Dealing with Production Cycles: DevOps teams typically use ML to understand and analyze resource utilization, among other patterns, to detect possibilities of abnormal patterns such as memory leaks. ML’s advantage of having a better understanding of application or production makes it more apt in managing production issues.
- Addressing Emergencies: ML’s key role here is analyzing machine intelligence. ML manages well with the production chain, especially in dealing with sudden alerts, by training systems continuously to identify repeating patterns and inadequate warnings, thus filtering the process of sudden alerts.
- Triage Analytics: ML has its way of dealing with analytics, where it can easily prioritize known issues and some unknown ones. ML tools can help you identify issues in general processing and also manage release logs to create coordination with new deployments.
- Early Detection: ML tools allow Ops teams to detect an issue at an early stage and ensure quicker response times, allowing business continuity. It can create all key patterns that can analyze and predict user behavior, such as configuration to meet the expected performance levels, response rate, and duration of success for a brand-new nature, and keep a continuous check on factors that can impact customer engagement.
- Business Assessment: ML not only supports process development but also plays a key role in ensuring an organization’s business continuity. While DevOps highly regards understanding code release to achieve business goals, ML tools deal with that with their pattern-based functionality by analyzing user metrics and alerting the concerned business teams and coders in case of any issue.
Moreover, Machine Learning (ML) can help DevOps in:
- IT Operations Analytics (ITOA)
- Predictive Analytics (PA)
- Artificial Intelligence (AI)
- Algorithmic IT Operations (AIOps)
After knowing the advantages that AI and ML offer to your DevOps environment, the next step is to look for steps to implement AI and ML in DevOps.
Here are seven steps that should be assessed to make the DevOps environment AI/ML-driven:
- Adopting Advanced APIs: Moving development teams to gain hands-on experience working with canned APIs like Azure, AWS, and GCP, which allow the deployment of robust AI/ML capabilities into their software without creating self-developed models. Further, they can focus on integrating add-ons such as voice-to-text and other advanced patterns.
- Identifying Related Models: The next step after the above would be identifying similar AI/ML APIs. With successful ML/AI model deployments, development becomes easier, and individual teams can work on further enhancements and apply them to additional use cases.
- Parallel Pipeline: Given that AI and ML are at the experimentation stage, it will also be important to consider running parallel pipelines so that things won’t go wrong in case of failure or sudden halts. A better way to deal with this would be to add ML/AI capabilities step-wise, gradually aligning with the projects’ progress and avoiding significant delays.
- Pre-trained Model: A well-documented, pre-trained model can drastically reduce the threshold for adopting ML and AI capabilities. A pre-trained model can be helpful in recognizing user behavior or inputs in a specific search. If it can at least match the basic aspects of the user search pattern, further add-ons to it can yield better results that can fully match the user behavior pattern. So, having a pre-trained model is key to AI/ML adoption at an initial phase.
- Public Data: Finding the initial training data is a key challenge in adopting AI/ML. None would feed this data. So, where will you drive information from? That’s where you require public data sets. It may not meet your requirements, but it can at least fill the gaps to enhance project viability.
- Due Identity: One starts witnessing the true potential of ML/AI only after the software runs and shows completion at a high rate, quality, and performance compared to the traditional approach. So, it will be necessary for any organization to identify and forward the success stories that they see out of AI/ML adoption to further teams, keeping them updated.
- Broaden Horizons: Developers should ideally be continuously seeking new knowledge and staying updated. This applies more to AI/ML use cases. To encourage this, organizations should encourage teams by easing their access to MI/AL sandboxes and general-purpose APIs without additional formalities that come as part of the corporate procurement process.
Conclusion
Overall, AI/ML has arrived to bridge the long gap between humans and huge volumes of data, otherwise known as Big Data.
Is it not helpful to have a tool that can provide a consolidated solution derived from widely available similar scenarios over the web instead of disturbing your entire software environment and applications just for a single log entry from heaps of log data entries?
Balancing the human capabilities to catch the velocities of expanding data scope and giving them operational intelligence that can quickly deal with the scenario is what AI and ML capabilities do, which you might require in your DevOps environment.
So, the solution is to have a system that can mimic user behavior, such as searching, monitoring, troubleshooting, and interacting with data, instead of analyzing unmeasurable data volumes.
Go ahead! It’s time you make your DevOps environment, AI-powered and ML-driven!