Increase in Tool Integration: The proliferation of projects and the corresponding need for more DevOps tools have led to a significant rise in the number of integrations between projects and tools. This complexity has prompted organizations to rethink their approach to adopting and integrating DevOps tools.
The evolution of DevOps has unfolded through four distinct phases, each addressing the growing demands and complexities of software development and delivery.
This four phases are as follows:
Phase 1: Bring Your Own DevOps (BYOD)
In the Bring Your Own DevOps phase, each team selected its own tools. This approach caused problems when teams attempted to work together because they were not familiar with the tools of other teams. This phase highlighted the need for a more unified toolset to facilitate smoother team integration and project management.
Phase 2: Best-in-class DevOps
To address the challenges of using disparate tools, organizations moved to the second phase, Best-in-class DevOps. In this phase, organizations standardized on the same set of tools, with one preferred tool for each stage of the DevOps lifecycle. It helped teams collaborate with one another, but the problem then became moving software changes through the tools for each stage.
Phase 3: Do-it-yourself (DIY) DevOps
To remedy this problem, organizations adopted do-it-yourself (DIY) DevOps, building on top of and between their tools. They performed a lot of custom work to integrate their DevOps point solutions together. However, since these tools were developed independently without integration in mind, they never fit quite right. For many organizations, maintaining DIY DevOps was a significant effort and resulted in higher costs, with engineers maintaining tooling integration rather than working on their core software product.
Phase 4: DevOps Platform
A single-application platform approach improves the team experience and business efficiency. A DevOps platform replaces DIY DevOps, allowing visibility throughout and control over all stages of the DevOps lifecycle.
By empowering all teams – Development, Operations, IT, Security, and Business – to collaboratively plan, build, secure, and deploy software across an end-to-end unified system, a DevOps platform represents a fundamental step-change in realizing the full potential of DevOps.
GitLab's DevOps platform is a single application powered by a cohesive user interface, agnostic of self-managed or SaaS deployment. It is built on a single codebase with a unified data store, that allows organizations to resolve the inefficiencies and vulnerabilities of an unreliable DIY toolchain.
How DevOps can benefit from AI and ML?
Artificial intelligence (AI) and machine learning (ML) are still maturing in their applications for DevOps, but there is plenty for organizations to take advantage of today. They assist in analyzing test data, identifying coding anomalies that could lead to bugs, as well as automating security and performance monitoring to detect and proactively mitigate potential issues.
AI and ML can find patterns, figure out the coding problems that cause bugs, and alert DevOps teams so they can dig deeper.
Similarly, DevOps teams can use AI and ML to sift through security data from logs and other tools to detect breaches, attacks, and more. Once these issues are found, AI and ML can respond with automated mitigation techniques and alerting.
AI and ML can save developers and operations professionals time by learning how they work best, making suggestions within workflows, and automatically provisioning preferred infrastructure configurations.
AI and ML excel in parsing vast amounts of test and security data, identifying patterns and coding anomalies that could lead to potential bugs or breaches. This capability enables DevOps teams to proactively address vulnerabilities and streamline alerting processes.
The evolution of DevOps has unfolded through four distinct phases, each addressing the growing demands and complexities of software development and delivery.
This four phases are as follows:
Phase 1: Bring Your Own DevOps (BYOD)
In the Bring Your Own DevOps phase, each team selected its own tools. This approach caused problems when teams attempted to work together because they were not familiar with the tools of other teams. This phase highlighted the need for a more unified toolset to facilitate smoother team integration and project management.
Phase 2: Best-in-class DevOps
To address the challenges of using disparate tools, organizations moved to the second phase, Best-in-class DevOps. In this phase, organizations standardized on the same set of tools, with one preferred tool for each stage of the DevOps lifecycle. It helped teams collaborate with one another, but the problem then became moving software changes through the tools for each stage.
Phase 3: Do-it-yourself (DIY) DevOps
To remedy this problem, organizations adopted do-it-yourself (DIY) DevOps, building on top of and between their tools. They performed a lot of custom work to integrate their DevOps point solutions together. However, since these tools were developed independently without integration in mind, they never fit quite right. For many organizations, maintaining DIY DevOps was a significant effort and resulted in higher costs, with engineers maintaining tooling integration rather than working on their core software product.
Phase 4: DevOps Platform
A single-application platform approach improves the team experience and business efficiency. A DevOps platform replaces DIY DevOps, allowing visibility throughout and control over all stages of the DevOps lifecycle.
By empowering all teams – Development, Operations, IT, Security, and Business – to collaboratively plan, build, secure, and deploy software across an end-to-end unified system, a DevOps platform represents a fundamental step-change in realizing the full potential of DevOps.
GitLab's DevOps platform is a single application powered by a cohesive user interface, agnostic of self-managed or SaaS deployment. It is built on a single codebase with a unified data store, that allows organizations to resolve the inefficiencies and vulnerabilities of an unreliable DIY toolchain.
How DevOps can benefit from AI and ML?
Artificial intelligence (AI) and machine learning (ML) are still maturing in their applications for DevOps, but there is plenty for organizations to take advantage of today. They assist in analyzing test data, identifying coding anomalies that could lead to bugs, as well as automating security and performance monitoring to detect and proactively mitigate potential issues.
AI and ML can find patterns, figure out the coding problems that cause bugs, and alert DevOps teams so they can dig deeper.
Similarly, DevOps teams can use AI and ML to sift through security data from logs and other tools to detect breaches, attacks, and more. Once these issues are found, AI and ML can respond with automated mitigation techniques and alerting.
AI and ML can save developers and operations professionals time by learning how they work best, making suggestions within workflows, and automatically provisioning preferred infrastructure configurations.
AI and ML excel in parsing vast amounts of test and security data, identifying patterns and coding anomalies that could lead to potential bugs or breaches. This capability enables DevOps teams to proactively address vulnerabilities and streamline alerting processes.