The days of human-intensive tasks are long gone as Artificial Intelligence (AI) and Machine Learning (ML) continue to transform DevOps. These technologies streamline industry processes, from developing a product to releasing it to the market and delivering a memorable end-user experience.
Automation has taken the world by storm as SMBs and large enterprises everywhere are making DevOps a critical part of their IT architecture. The technology breaks down silo walls that are every developer’s nightmare, with AI and ML strongly supporting this goal. Without a doubt, this brings up the million-dollar question: what good is AI/ML for DevOps?
The Future Of Work
With the pace of technology change, companies should see how emerging technologies like AI and ML can revamp DevOps to speed up decision making and boost profitability. It is all about aiming for results beyond human capability. Here are some suggestions on how you can start:
- Automation For Recurring Problems
Using a feedback loop, you can train AI and ML algorithms to automatically deploy solutions that monitor redundancy.
- Refining Deployment Techniques
Leverage Artificial Intelligence metrics and analytics to maintain a healthy application deployment life cycle in private, public, and hybrid cloud environments.
- Analyzing & Processing Data
If you come across issues such as a lack of freely-available and accessible data, then AI is the way to go. This will earn you access to large volumes of online information that is a gold mine for Big Data aggregation.
You can also use AI and ML to help developers make technical sense of the data that rests across multiple data warehouses. It will become easier for your developers to understand an error and get details of the events that led to the fault occurrence. The two technologies can enable the shift from diagnostics to prognostics across systems and tools which further simplifies the task of anticipating, detecting, and resolving errors.
- Data Retention & Historical Tracking
Backtracking to a specific time and studying patterns on a new interpretation to see how it happened is not simple. The data analysis capabilities of AI and ML map a trajectory and keep every piece of information as a reference. This means you do not have to start from scratch every time. Your system can get as close to the desired outcome as possible, so you not only build a pattern but also forge a path to attain that pattern.
Addressing DevOps Challenges
AI and ML help DevOps prioritize innovation by taking care of gaps and discrepancies across the operational lifecycle. They allow teams to smartly manage data speed, volume, and variability, resulting in better efficiency.
There are some complex challenges that both technologies address. Let’s have a look at them.
This refers to the process of transforming log files into data and making intelligent decisions based on that data. Teams generate a variety of logs including OS logs, application logs, CDN traffic, server uptimes, database queries, etc. All these drive data-driven decision making and are especially beneficial for DevOps and reliability engineers.
Log analysis utilizes intelligent methods to classify information such as absorbing huge amounts of unstructured and noisy data and grouping them into actionable data sets. It actively detects and reports any external data points that do not come within the regular clusters. Also, it is capable of foreseeing the possible consequence of an activity. For example, in clustered server environments, log analysis tools study the likelihood of associated services failing, giving teams time to prepare a proper backup. These tools can train ML algorithms to analyze logs, and other use cases range from identifying patterns to guide business decisions and general security.
Any critical failure in a specific DevOps tool or area can hamper progress and lead to cycle delays. Fortunately, AI can study patterns and predict the chances of failure especially if a fault that has occurred is known to generate specific readings. It can also identify indicators beyond human perception. These early predictions and notifications enable DevOps to proactively fix any issues before they can affect the software development life cycle, hence preventing or reducing downtimes.
Sometimes, engineers do not investigate failures in detail because they primarily focus on going live. They tend to avoid detailed root cause analysis which causes problems later on. In such cases, even if they come up with a solution to an issue, the root cause remains unknown. AI plays an integral role in coming up with a permanent solution to a problem by helping teams conduct root cause analysis.
Although DevOps guarantees automation and consistency in release processes, certain areas still require a person to intervene. The benefit of AI is that it automates mundane and repeatable tasks in DevOps, effectively accelerating the complete process. This frees up valuable IT resources to focus on creativity, key tasks, and reducing error.
Artificial Intelligence is also useful if businesses are not entirely ready to let computers manage themselves. It can suggest solutions for writing cleaner, more efficient, and performant code. It can even emphasize the expected impact of a change, giving DevOps teams a clear direction when they sync up with one another on what to address next.
Spotting flows immediately requires DevOps teams to have a proper alert system in place. It is not uncommon for alerts to come in huge numbers and flood DevOps systems with the same severity. This makes it difficult for teams to react and respond to these alerts as soon as they can. AI and ML prioritize team responses based on factors like past behavior as well as the source and intensity of an alert. In this way, systems are not overwhelmed and can function smoothly.
Security As The Principal Focus
Security occupies the limelight when it comes to employing a DevOps approach. This brings us to DevSecOps which holds all teams involved in the software delivery lifecycle accountable for security. Integrating security and compliance monitoring tools into every stage of the development process guarantees that they keep up with the speed and agility of DevOps. On the other hand, security is one of the main obstacles to quick and effortless system development and deployment. The reason is experts have not developed most security solutions to test and code at the pace that DevOps demands.
This is where implementing automation from the very beginning reduces the need for manual configuration. In turn, businesses minimize the probability of mismanagement and fault occurrence that can translate into downtime or potential breaches. A tried and tested approach to protecting one’s business is to frequently patch and update software.
So, how are AI and ML relevant? These platforms can analyze usage data and security threats to optimize enterprise applications. They investigate user behavior to identify high-performance application functions and modules, allowing teams to improve the end-user experience in these areas. Furthermore, when it comes to tracking security vulnerabilities with AI, we can easily pinpoint where hackers are attempting to breach our systems, allowing us to prepare ourselves promptly. For instance, you can opt for a decision engine to reduce the impact on business operations in the event of a denial-of-service attack.
It does not stop here. AI also churns through information in real-time to detect fraudulent or suspicious activity related to unusual data trends and patterns.
AI and ML are futuristic technologies that streamline work and add to an enterprise’s bottom line. Fusing DevOps, AI, and ML into one dependent, long-term model can produce consistent ROI while ensuring IT operations efficiency. This maximizes productivity, predicts performance roadblocks, and solves potential problems quickly before they become real issues to contend with.
At Clouve, we help you embrace these possibilities with premium tools and solutions that offer real business value. Feel free to contact us today to learn more about what we can do for you!