Ops and Types
Why are there so many Ops terms and do we need them?
Technology-driven companies use many sophisticated technical environments. They use many processes that are unique to them and require proper descriptions. Hence, they need to come up with coherent terminologies that help them define their tasks and procedures correctly.
However, this task is not easy. It isn’t easy to find suitable terminology to describe their workflows. This becomes even more difficult if one is working with a big team. Managing and communicating with many people is anyways trouble, so having a standard system is essential.
This has led to the rise of the “Ops” suffix culture. It started as a merger of Development (Dev) and Information Technology Operations (Ops) and has extensively been used since.
i. AIOps
Algorithmic IT Operations synonymously titled as “Artificial Intelligence for IT Operations.” Replaces manual IT operations tools with an automated IT operations platform that collects IT data, identifies events and patterns, and reports or remediates issues — all without human intervention.
Good to read material: AIOps
ii. AnalyticsOps
AnalyticsOps is a framework that oversees development of automation and consumption of analytics across the enterprise. It’s a series of steps, integrated processes and technologies that are must-haves for companies that want to successfully deliver business value from AI and advanced analytics-based models.
Good to read material: AnalyticsOps
iii. AppOps
The application developer is also the person responsible for operating the app in production; the operational side of application management, including release automation, remediation, error recovery, monitoring, maintenance.
Good to read material: AppOps
iv. ChatOps
ChatOps is the use of chat clients, chatbots and real-time communication tools to facilitate how software development and operation tasks are communicated and executed. In a ChatOps environment, the chat client serves as the primary communication channel for ongoing work. The use of chat clients, chatbots and real-time communication tools to facilitate how software development and operation tasks are communicated and executed.
Good to read material: ChatOps
v. CloudOps
CloudOps is short for Cloud Operations, and is the process of identifying and defining the appropriate operational procedures to optimize IT services within the cloud environment. It is the culmination of DevOps and traditional IT operations applied to cloud-based architectures. Attain zero downtime based on “continuous operations”; run cloud-based systems in such a way that there’s never the need to take part or all an application out of service. Software must be updated and placed into production without any interruption in service.
Good to read material: CloudOps
vi. DataOps
A collection of data-analytics technical practices, workflows, cultural norms and architectural patterns that enable: rapid innovation and experimentation; extremely high quality and very low error rates; collaboration across complex arrays of people, technology, and environments; and clear measurement, monitoring and transparency of results. In a nutshell, DataOps applies Agile development, DevOps and lean manufacturing to data-analytics (Data) development and operations (Ops).
Good to read material: DataOps
vii. DataSecOps
A data management method that emphasizes communication, collaboration, integration and automation of processes between data engineers, scientists and other data professionals. DataSecOps is an agile, holistic, security embedded approach to coordinating of the ever-changing data and its users, aimed at delivering quick data to value while keeping data private, safe and well-governed.
Good to read material: DataSecOps
viii. DevOps
A set of practices that combines software development (Dev) and information-technology operations (Ops) that aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes.
Good to read material: DevOps
ix. DevSecOps
DevSecOps — short for development, security, and operations — automates the integration of security at every phase of the software development lifecycle, from initial design through integration, testing, deployment, and software delivery. Views security as a shared responsibility integrated from end to end. Emphasizes the need to build a security foundation into DevOps initiatives.
Good to read material: DevSecOps
x. GitOps
Use of an artifact repository that always contains declarative descriptions of the infrastructure currently desired in the production environment and an automated process to make the production environment match the described state in the repository.
Good to read material: GitOps
xi. InfraOps
The layer consisting of the management of the physical and virtual environment, which may very well be within a cloud environment. On top of this layer would be Service Operations (‘SvcOps’) and Application Operations (‘AppOps’).
Good to read material: InfraOps
xii. SvcOps
Service operation encompasses the day-to-day activities, processes, and infrastructure responsible for delivering value to the business through technology. In Service Strategy, Service Design, Service Transition and Continual Service Improvement, we create value. But, no service is consumed and no business activity is experienced. Service operation encompasses the day-to-day activities, processes, and infrastructure responsible for delivering value to the business through technology.
Good to read material: SvcOps
xiii. MLOps
MLOps or ML Ops is a set of practices 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.
Good to read material: MLOps
xiv. ModelOps
ModelOps, as defined by Gartner, “is focused primarily on the governance and life cycle management of a wide range of operationalized artificial intelligence and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models”.
Good to read material: ModelOps
xv. NoOps
A NoOps environment quite literally means no operations. NoOps is when an IT environment becomes so automated from the underlying infrastructure — through technologies including artificial intelligence (AI) and machine learning — that there’s no need for a dedicated team to manage software in-house.
Good to read material: NoOps
xvi. DecisionOps
The decision operation defines which rules from a given branch are part of the ruleset. You choose which decision operation to use when creating a test suite, simulation, or deployment configuration. A ruleset includes rule artifacts and other elements. A decision operation includes all the settings needed to define the contents of a ruleset and its parameters. The ruleset content and parameters allow the client application to exchange information with the ruleset.
Good to read material: DecisionOps
Note: The contents above are collated from different sources and all credits goes to them.
