Otter is an infrastructure automation tool that runs under Microsoft Windows, designed by the software company Inedo. Otter utilizes Infrastructure as Code to model infrastructure and configuration.
Software deployment is all of the activities that make a software system available for use.
Release engineering, frequently abbreviated as RE or as the clipped compound Releng, is a sub-discipline in software engineering concerned with the compilation, assembly, and delivery of source code into finished products or other software components. Associated with the software release life cycle, it was said by Boris Debic of Google Inc. that release engineering is to software engineering as manufacturing is to an industrial process:
Release engineering is the difference between manufacturing software in small teams or startups and manufacturing software in an industrial way that is repeatable, gives predictable results, and scales well. These industrial style practices not only contribute to the growth of a company but also are key factors in enabling growth.
Application lifecycle management (ALM) is the product lifecycle management of computer programs. It encompasses requirements management, software architecture, computer programming, software testing, software maintenance, change management, continuous integration, project management, and release management.
Build automation is the process of automating the creation of a software build and the associated processes including: compiling computer source code into binary code, packaging binary code, and running automated tests.
Puppet is a software configuration management tool which includes its own declarative language to describe system configuration. It is produced by Puppet Inc., founded by Luke Kanies in 2005. Its primary product, Puppet Enterprise, is a proprietary and closed-source version of its open-source Puppet software. They use Puppet's declarative language to manage stages of the IT infrastructure lifecycle, including the provisioning, patching, configuration, and management of operating system and application components in data centers and cloud infrastructures.
AnthillPro is a software tool originally developed and released as one of the first continuous integration servers. AnthillPro automates the process of building code into software projects and testing it to verify that project quality has been maintained. Software developers are able to identify bugs and errors earlier by using AnthillPro to track, collate, and test changes in real time to a collectively maintained body of computer code.
Progress Chef is a configuration management tool written in Ruby and Erlang. It uses a pure-Ruby, domain-specific language (DSL) for writing system configuration "recipes". Chef is used to streamline the task of configuring and maintaining a company's servers, and can integrate with cloud-based platforms such as Amazon EC2, Google Cloud Platform, Oracle Cloud, OpenStack, IBM Cloud, Microsoft Azure, and Rackspace to automatically provision and configure new machines. Chef contains solutions for both small and large scale systems.
Release management is the process of managing, planning, scheduling and controlling a software build through different stages and environments; it includes testing and deploying software releases.
DevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development (Dev) and IT operations (Ops) as a means for improving and shortening the systems development life cycle.
Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate. Continuous testing was originally proposed as a way of reducing waiting time for feedback to developers by introducing development environment-triggered tests as well as more traditional developer/tester-triggered tests.
Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and, following a pipeline through a "production-like environment", without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production. A straightforward and repeatable deployment process is important for continuous delivery.
Application-release automation (ARA) refers to the process of packaging and deploying an application or update of an application from development, across various environments, and ultimately to production. ARA solutions must combine the capabilities of deployment automation, environment management and modeling, and release coordination.
In software engineering, service virtualization or service virtualisation is a method to emulate the behavior of specific components in heterogeneous component-based applications such as API-driven applications, cloud-based applications and service-oriented architectures. It is used to provide software development and QA/testing teams access to dependent system components that are needed to exercise an application under test (AUT), but are unavailable or difficult-to-access for development and testing purposes. With the behavior of the dependent components "virtualized", testing and development can proceed without accessing the actual live components. Service virtualization is recognized by vendors, industry analysts, and industry publications as being different than mocking. See here for a Comparison of API simulation tools.
BuildMaster is an application release automation tool, designed by the software development team Inedo. It combines build management and ARA capabilities to manage and automate processes primarily related to continuous integration, database change scripts, and production deployments, overall releasing applications reliably. The tool is browser-based and able to be used "out-of-the-box". Its feature set and scope puts it in line with the DevOps movement, and is marketed as "more than a release automatigs together the people, processes, and practices that allow teams to deliver software rapidly, reliably, and responsibly.” It's a tool that embodies incremental DevOps adoption.
XebiaLabs is an independent software company specializing in DevOps and continuous delivery for large enterprise organizations. XebiaLabs offers a DevOps Platform for application-release automation (ARO). These components include release orchestration, deployment automation and DevOps intelligence.
Infrastructure as code (IaC) is the process of managing and provisioning computer data center resources through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. The IT infrastructure managed by this process comprises both physical equipment, such as bare-metal servers, as well as virtual machines, and associated configuration resources. The definitions may be in a version control system, rather than maintaining the code through manual processes. The code in the definition files may use either scripts or declarative definitions, but IaC more often employs declarative approaches.
Continuous configuration automation (CCA) is the methodology or process of automating the deployment and configuration of settings and software for both physical and virtual data center equipment.
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous development practice of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.
TestOps refers to the discipline of managing the operational aspects of testing within the software delivery lifecycle.
