By now you probably know that Continuous Deployment (CD) is an increasingly popular development process that enables teams to quickly deploy and update code without manual intervention. CD has revolutionized how software delivery happens, and it has become crucial for streamlining the release process, accelerating time-to-market, increasing stability, improving developer and customer experiences, and scaling business growth. CD helps ensure that the right code is deployed in the right environments with minimal effort, drastically reducing the time and cost associated with software delivery.
CD will continue playing a major role in software delivery in the future as CI/CD processes become an integral part of modern development workflows. The process will become more automated, as artificial intelligence and machine learning are used to automate tasks such as testing and environment creation and promotion.
Continuous Deployment has already revolutionized how teams develop and deploy code, and it will continue to evolve in order to meet the needs of increasingly complex software delivery processes.
Let’s take a look at emerging artificial intelligence and the role it could play in the future of Continuous Deployment.
Imperative to Declarative to Generative?
Generative AI is an application of machine learning that can create a variety of content such as text, images, or audio from natural language prompts. Think ChatGPT (Generative Pre-trained Transformer). The introduction of ChatGPT has shown the world what some possibilities hold for our future.
Some of the biggest challenges we hear from our extensive and varying user base are the complexities and difficulties when it comes to configuring, deploying and securing infrastructure and applications across multiple platforms, clouds, geographies, and environments. Not to mention the myriad languages they often have to support on top of everything.
The industry as a whole adopted APIs as the primary means of communication and configuration. This brought us imperative APIs that issued commands via HTTP and no long via CLI. Software delivery was no different. Imperative pipelines became the norm to ship code.
It wasn’t very long after the introduction of imperative APIs that people felt the burden and started adopting declarative APIs, which brought with it configuration-as-code, and as you guessed it, declarative deployment. The short version of imperative v. declarative is defining what you want v. declaring the outcome you want.
While many are still grappling with imperative deployment while moving their sights to declarative deployment, we’ve already started to experiment with generative deployment. As models become more reliable and better trained based on well-formed specifications, we should be able to state what we want to do along with the outcome, and the AI should produce the required configuration.
Getting to the deployment aspect, we should be able to tell the AI to, not only produce the configuration but also to deploy it to a number of environments. Heck, even create the environments first if needed. After all Continuous Deployment is all about automation and AI promises automation to the fullest extent possible.
Here’s a kicker: today’s AI can already produce the configuration and some of the automation required to deploy the generated configuration. It’s not exactly production grade or ready for primetime but experimenting is fun and opens up a world of possibilities and excitement. Since we as an industry have adopted APIs as our primary means of communication we can technically automate anything. I mean have you stepped into a driverless “cab” yet? The future is here!
Keep in mind that we as humans still program the AI. We just program it to be incredibly fast. Mainly, so we can focus our time and energy on more strategic projects and competitive differentiation. So, don’t fret your job as a developer or engineer is going to the robots.
No, quite the opposite.
We’re finding many neat ways to elevate our development practices, communication and collaboration, using different AI technologies. A fun example is writing comments in your code. That can be difficult when you’re a global team attempting to communicate with a single primary language when that’s not everyone’s first or most comfortable language. But, leveraging generative AI some of the best comments are coming from devs that speak English as their second, third or even fourth language.
Ultimately, Continuous Deployment is a powerful tool for organizations looking to stay agile and competitive in today’s digital world. By embracing automation, collaboration and best practices, organizations can ensure their code changes are deployed quickly and without errors. This will result in faster time-to-market for new features and improved customer experience overall, all with a developer-first experience that elevates development practices and increases stability. We’re excited to bring the next generation of Continuous Deployment when the time is right!
But for now, we’ll continue to bring you the industry’s first and top declarative deployment orchestration solution. Check it out and try for yourself today. We’d love to hear your thoughts so contact us anytime.
