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You're reading from  Enterprise DevOps for Architects

Product typeBook
Published inNov 2021
Reading LevelBeginner
PublisherPackt
ISBN-139781801812153
Edition1st Edition
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Python
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Author (1)
Jeroen Mulder
Jeroen Mulder
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Jeroen Mulder

Jeroen Mulder is a certified enterprise and security architect, and he works with Fujitsu (Netherlands) as a Principal Business Consultant. Earlier, he was a Sr. Lead Architect, focusing on cloud and cloud native technology, at Fujitsu, and was later promoted to become the Head of Applications and Multi-Cloud Services. Jeroen is interested in the cloud technology, architecture for cloud infrastructure, serverless and container technology, application development, and digital transformation using various DevOps methodologies and tools. He has previously authored “Multi-Cloud Architecture and Governance”, “Enterprise DevOps for Architects”, and “Transforming Healthcare with DevOps4Care”.
Read more about Jeroen Mulder

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Chapter 9: Integrating AIOps in DevOps

So far, we've looked at automating development from a DevOps perspective and have automated operations. The next step is artificial intelligence (AI)-enabled DevOps. DevOps engineers manage multiple libraries and various pipelines. To speed up digital transformation, it's crucial that issues are detected and remediated fast. AI can also be of great added value in these DevOps processes. In this chapter, you will learn how to implement AI-enabled DevOps and enable rapid innovation.

After completing this chapter, you will have a good understanding of the various steps that need to be taken to implement and integrate AI-driven pipelines for development and deployment. You will be introduced to some major tools and will learn the requirements to implement these as part of the innovation of digitally transforming enterprises.

In this chapter, we're going to cover the following main topics:

  • Introducing AI-enabled DevOps...

Introducing AI-enabled DevOps

In the previous chapter, we studied the AIOps platform, concluding that it will help operators in getting rid of tedious, repetitive tasks, detecting and solving issues faster, and enabling more stable systems. Stability and resilience are still the key aspects operators strive for with IT systems, yet new features and changes to the systems are being developed and launched at an increasing speed. If AI can help operations, it can also help development. This section will explain why AI-enabled DevOps will help in creating better systems at a higher velocity.

AI can help developers monitor and detect issues in their builds faster than if this were done only manually or even in an automated process, without the power of AI. With AI, it's possible to continuously monitor code changes, compare these to other code building blocks, and swiftly detect issues. But AI will also enable predictive mitigations: it will learn how certain code changes may impact...

Enabling rapid innovation in digital transformation

The majority of modern enterprises are well underway in transforming their business to make it more digital native. We talked about this extensively in the first two chapters of this book. Customers continuously demand new features, and they want these features to be delivered almost instantly. To control this process, enterprises need to develop an innovation strategy catering for rapid innovation. An innovation strategy can be depicted as a pyramid, where AI-driven innovation is at the very peak of this pyramid.

This can be seen in the following diagram:

Figure 9.2 – Pyramid of AI-enabled innovation

Enterprises do not get to the top of the pyramid in one go; they usually start at the bottom, where innovation is driven by cost savings. From there, they need to develop the next steps, resulting in rapid innovation using AI and ML.

The first steps typically involve ways to find a budget, which...

Monitoring pipelines with AIOps

In this section, we will study AI-driven technology that will help developers in monitoring and improving their CI/CD pipelines. Let's recap on the principle of a pipeline first. A pipeline should be seen as a workflow: it guides code through a process where it's tested and eventually deployed to a platform. Following this process, code will be pushed to different levels in the promotion path: development, testing, acceptance, and production. This process can be automated.

At the start of this process, and thus the pipeline, there is a repository where the various components of systems are stored. Since everything is code, the repository will hold code for applications, infrastructure components, configuration templates, and scripts to launch APIs. While building a system through a pipeline, DevOps software will make sure that the appropriate components are pulled from the repository and compiled into packages that can be deployed. A common...

Assessing the enterprise readiness of AI-enabled DevOps

So far, we've learned that digital transformation is a process. It doesn't come in one go; the enterprise needs to be prepared for this. It includes adopting cloud platforms and cloud-native technology. Enterprises will have legacy systems and likely a lot of data sitting in different silos, leaving the enterprise with the challenge that this data is used in an optimized way. It's a misperception to think that AI-enabled tools and data science can solve this issue from the beginning.

The enterprise will need to have a complete overview of all its assets, but also its skills and capabilities. First, data specialists will need to assess the locations, formats, and usability of data sources. The data scientists then will have to design data models. They can't do this in isolation: they will have to collaborate with DevOps engineers and the application owners to agree on things such as version control, model...

Summary

In this chapter, we learned how to integrate AI and ML into our DevOps pipelines. We discussed the basic requirements and steps for implementing AI-enabled DevOps, starting with access to source repositories, creating data lakes, initiating and training data models, and follow-up recommendations and actions. We also learned that AI-enabled DevOps is a stage in digital transformation, but that enterprises need to set out a roadmap that eventually allows them to integrate AI and ML into their development and deployment processes. AI-driven development and operations are at the peak of innovation in digital transformation.

Next, we introduced some tools that will help us in implementing AI-enabled DevOps. We learned that it's a fast-growing market where major cloud providers try to integrate their native DevOps tools with AI and ML. Examples include Kubeflow by Google, CodeGuru by AWS, and MLOps by Microsoft Azure.

Finally, we discussed the readiness assessment for...

Questions

  1. We introduced the innovation pyramid for digital transformation. What is the base platform of this pyramid?
  2. Integrating AI into DevOps pipelines is typically done through containerization. We discussed a container orchestration tool that allows us to agnostically deploy containers to various platforms. What is this tool called?
  3. Name three possible outcomes/results of AI-enabled DevOps, specifically for improving code.

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Author (1)

author image
Jeroen Mulder

Jeroen Mulder is a certified enterprise and security architect, and he works with Fujitsu (Netherlands) as a Principal Business Consultant. Earlier, he was a Sr. Lead Architect, focusing on cloud and cloud native technology, at Fujitsu, and was later promoted to become the Head of Applications and Multi-Cloud Services. Jeroen is interested in the cloud technology, architecture for cloud infrastructure, serverless and container technology, application development, and digital transformation using various DevOps methodologies and tools. He has previously authored “Multi-Cloud Architecture and Governance”, “Enterprise DevOps for Architects”, and “Transforming Healthcare with DevOps4Care”.
Read more about Jeroen Mulder

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