DevOps / Machine Learning / Sponsored / Contributed

5 Cloud Automation Tips for Developers and DevOps

16 Mar 2021 9:44am, by

Saif Gunja
Saif is Director of Product Marketing for Dynatrace’s Cloud Automation and DevOps solutions, bringing over 10 years of IT and marketing experience from his previous roles at VMware, Apple and Deloitte.

Across industries, digital teams are struggling to keep up with the complexity of cloud native architectures and increasing customer demand for digital services. The pressure to provide fast, reliable and compelling experiences has intensified over the last year and shows no signs of slowing down.

To deliver these experiences while maintaining a pipeline of fast, high-quality software release cycles, organizations are increasingly adopting cloud native architectures. However, as long as organizations remain boxed in by manual processes, siloed teams and limited lifecycle visibility, managing these complex environments and delivering on digital transformation objectives will continue to be difficult.

The gap between constrained IT resources and growing cloud complexity will only get wider. Developers, DevOps and SRE teams should be empowered with a cloud automation approach that facilitates orchestration of development processes, including testing and quality gates, and ensures shorter release cycles, faster delivery, and higher quality software. By embracing a highly automated, AI-driven DevOps practice, DevOps and SRE teams can produce the application releases their customers expect at the pace they require.

Here are five cloud automation enhancements that organizations can leverage to empower their digital service teams and accelerate the shift to an AI-driven DevOps approach.

1. AI-Assisted Release Version Analysis

Leveraging AI-assistance to continuously analyze releases in production enables digital teams to swiftly and automatically address vulnerabilities as they arise between release iterations. When developers, DevOps and SRE teams can compare performance across release versions quickly and easily, troubleshooting and identifying root causes is that much simpler. This also ensures that teams can roll back release updates and restore the most stable version for users.

Release-risk insights, automated cross-version analysis, and AI-powered observability of full application lifecycles ensure teams save time and resources that they would otherwise spend on manual, tedious, and expensive war rooms.

2. Automatic and Intelligent Observability

Cloud automation is all about automatically determining if and when applications are ready to move to the next stage of their lifecycle, ensuring developers spend less time troubleshooting and more time driving innovations. That means orchestrating CI/CD pipelines, software testing and delivery, and remediation — all within the same platform.

But DevOps and SRE teams can’t shift that resource burden and free up more time for developers to innovate without an AI-powered approach to observability. Continuous monitoring and analysis of application health, in pre-production and production and across all cloud environments, ensures precise root-cause determination and continuously optimized code.

3. Automated SLO Validation with Closed-Loop Remediation

When digital teams “shift left” the testing for SLOs in production, it’s easier to find SLO violations and errors earlier in the development lifecycle. Consequently, these become more cost-effective to resolve. Remediation should go together with a delivery pipeline that incorporates intelligent quality gates and closed-loop remediation. With this approach, if a new software release fails in production, it’s automatically put into a continuous testing loop until all errors are resolved. Automatic remediation runbooks that buck the need for manual intervention ensure improved overall production quality and allow code to move smoothly to users through the delivery pipeline.

4. Integrated, End-to-End, Unified Platform Approach

More efficient collaboration across all teams — developers, CloudOps, ITOps, DevOps, SRE — requires everyone to work within a single platform, providing a baseline across preproduction and production environments. This kind of integration makes it possible for any range of teams working in concert with each other to act as a force multiplier, leveraging complete visibility across the environment to optimize productivity.

5. Open Source Initiatives

Finally, what’s necessary to power this cloud automation module is an open source approach to cloud native application lifecycle orchestration. Digital service teams need to rethink their model for software development and operations to ensure accelerated, at-scale delivery of modern cloud native applications. Open source cloud automation enhancements accelerate software delivery with automated orchestration of CI/CD and remediation pipelines, as well as reduce risk with more resilient software through AI-powered quality gates.

This enterprise-grade approach empowers digital teams to deliver high-quality applications at a faster pace. That’s the next-level jump required to create an automated, AI-driven DevOps practice that can deliver the compelling and secure cloud native applications users need at the speed they expect.

Photo by Ramaz Bluashvili from Pexels