How AI and Automation are Transforming DevOps Workflows

Artificial intelligence (AI) and automation are inviting new changes in every field that are surpassing every expectation. These technologies are on the path of bringing positive transformations to DevOps workflows.

In fact, when AI and automation are integrated into various stages of the DevOps lifecycle, productivity and positive outcomes improve. In this article, let’s explore further how to utilize AI and Automation in DevOps workflows.

When the usage of AI technologies in the DevOps market is viewed, it shows that this market will grow tremendously. In 2023, this market stood at $2.9 billion. However, the numbers are fascinating for the next seven to eight years as this market will mature to $24.9 billion by 2033. It will grow at a compound annual growth rate (CAGR) of 24%. 

This data portrays that more businesses will be attracted to this field.

Continuous Integration and Continuous Deployment (CI/CD) Operating on AI

AI is implemented in the CI/CD pipelines to automate various tasks. These are 

1. Code integration
2. Testing
3. Deployment 

For example, AI has the power to see when code changes happen, which further leads to integration issues. This alerts the coding team in advance so they can act on them instantly. 

Due to this predictive capability, there will be the following:

1. Fewer build failures
2. Non-stop deployment cycles

Data Insights: According to a survey, 33% of companies favor using AI in their operations and have started using it. This report further states that 42% of organizations are contemplating AI adoption.

Focus on Developing Intelligent Monitoring

AI is a force that can analyze a large amount of data. Companies are using this technology to detect errors in advance and analyze incidents before they occur. 

When historical data and patterns are identified, the development teams can get ready with their action plans.

Due to this, the system can have the following:

1. Fewer downtime
2. 100% system reliability

Not only is there intelligent automation, but AI can automate incident responses. Following this technique can lower the average time required to solve the complaint.

Data Insights: According to a report, companies will invest in DevOps automation to have a robust:

1. Compliance management (55%)
2. Infrastructure management (52%)
3. Performance optimization (51%)

AI Bringing Resource Allocation to DevOps Systems

Development systems can fail when they are overburdened. Therefore, AI can relieve the systems by learning their performance and workload patterns.

Furthermore, if additional resources are required, the team can be informed so that they can arrange them.

Due to this, companies can extract the following results:

1. More cost savings
2. Improved system performance

Data Insight: As per the official reports, 56% of organizations have applied AI in their workflows. Due to this, they have witnessed that they can save more on their systems.

A Greenlight to Code Quality

Coders can develop far better products when they are provided with platforms that can address their challenges on the spot.

There are various AI coding assistants, one of which is GitHub Copilot. These websites have the tendency to provide assistance in code development by AI tools.

These platforms let human coders focus on brainstorming other core activities rather than writing codes.

As a matter of fact, organizations that are utilizing these technologies are able to achieve more productivity with smaller teams.

Data Insights: More than 77,000 organizations are leveraging Microsoft’s GitHub Copilot to automate their coding processes.

Leveraging Predictive Analytics to Achieve More Performance

Predictive analytics is a term that is used in the DevOps ecosystem to view how many system challenges and performance issues are there. 

Your development team can work on the new trends that are extracted by the AI. Additionally, this technology will aid in building a robust work system, which DevOps teams can leverage to implement improvements.

Data Insights: ​

1. Big banking institutions, such as JPMorgan Chase, have implemented AI coding assistants. Due to this activity, the bank has increased its software engineers’ efficiency by 20%.
2. If we look at the Indian IT ecosystem, AI has the potential to increase productivity by 45% in the next five years.

Concrete Collaboration and More Knowledge Sharing

Knowledge shouldn’t stay in one place and should be distributed to everyone. AI fulfills this purpose for various organizations by developing knowledge bases and creating a proper repository for them.

For example, ChatGPT Projects was launched by OpenAI so that organizations can store their coding material in one place. Furthermore, they can use this feature to generate more codes.

When AI is properly used in the DevOps system, then there are:

1. Fewer knowledge gaps
2. Powerful knowledge base and articles to refer to
3. Proper training plans for the development teams

Data Insights:

1. Atlassian officially launched an AI assistant called Rovo last year. This tool saves several hours in generating content, such as a knowledge base, which new developers might require in the future.
2. In a report, Amazon has raised concerns that its developers are performing coding for only one hour a day. The rest of the time, they are busy building various knowledge bases and articles. Additionally, they update repositories.

Performing Root Cause Analysis

AI is seen as a tool that can help with coding. However, it is more than that. This technology can let the DevOps teams know what happened during a mishap.

The AI system thoroughly checks everything from the beginning to know the origin. It analyzes various log and system data to derive a conclusion about the incident. 

Due to this, there is no requirement of manual intervention and companies can solidify their systems so that this event doesn’t repeat.

100% Security and Compliance

AI builds a safety wall that protects the data from any breaches in the DevOps workflows. It stringently follows regulatory standards.

If it finds any unusual activities, it can tackle them under the set rules.

By chance, if any unfortunate event, such as data leakage, happens, artificial intelligence will scan the complete situation and develop an incident report that the company can show to its stakeholders.

A Positive Step Towards IT Operations with AIOps

Artificial Intelligence for IT Operations (AIOps) platforms are generated for a purpose. They utilize AI to automate their data center management. 

When AIOps is trained on enormous data, then it is ready to find, diagnose, and bring resolution to IT issues.

The company reaps the benefit of having high system performance, and there is reduced downtime.

Using MLOps for Better Productivity

Machine Learning Operations (MLOps) might sound strange, but they are gaining popularity.

It helps deploy and maintain machine learning models in code production. 

Global companies are using this technology for the following purposes:

1. Automate the deployment
2. Keep a close watch on ML models
3. Proper management of ML models

Conclusion

Undoubtedly, AI and automation in DevOps are trending everywhere. As per recent statistics 90% of businеss lеadеrs agrее that automation improves thе dеcision-making process.

Every organization wants to implement them so that they don’t have to work on repetitive tasks.

The other reason is that they want a system that predicts any type of potential issues.

Additionally, AI can read and write codes to build a powerful product for a company.

We have already mentioned the complete story in our above blog, which you can use to bring positive changes to your organization.

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