Code Stream provides the ability to model and visualize the release process of any type of software (applications, blueprints, workflows, configuration files, Puppet manifests, Chef recipes, and so on). All stakeholders (devs, operations teams, release engineers, and so on) can go to a central place to track the state of application and software releases. Users can view not just the history but exactly where builds and releases have failed. Code Stream and vRA use the same delivery platform (the same virtual appliance) and share many common services, including capturing release processes that IT is struggling with or spending a lot of time on, and automating those via vRCS and vRA.

As opposed to acquiring, integrating, and managing different solutions for provisioning and release automation, customers can benefit from a single solution. By integrating with artifact repositories such as JFrog Artifactory, Code Stream can manage and track the multiple artifact versions that are generated with new releases, and track their deployment across various environments. As companies release new app versions more often, the risk of deploying the wrong artifact version and breaking production increases significantly. To help customers have visibility into and tie all of their different repositories (Yum, NuGet, Nexus, and so on) into one place for troubleshooting and security auditing, it is important to track which artifacts have been deployed where. Code Stream can work for both Java/Linux and .NET/Windows shops and their respective toolsets. Certain release management tools (for example, Octopus, Microsoft Release Manager, and Chef Delivery) support only one technology or are optimized for one technology or certain toolsets. Code Stream does not prescribe any particular toolset, meaning it can support different teams using different technologies across the enterprise. In fact, it can be even be used for the life cycle of software that is not an application: scripts, workflows, blueprints, and so on.

In the next chapter, Chapter 10, Transforming VMware IT Operations Using ML we will learn about how to manage different cloud models from a single console and about the phase-wise transformation of data center operations methodologies. We will also learn how to design scalable infrastructure to host both legacy and new cloud native apps on unified platforms using ML-based solutions.