Abhishek k
4 min readMay 22, 2021

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A Review on MLOps, AIOps & DataOps

Introduction

As technology develops, more and more companies and organizations tend to migrate to digital solutions for their operational needs. The base of such digital functionality is Information Technologies (IT). Combining IT with the need for organization, management, and efficiency among the company, the end result is IT Operations, also known as ITOps. They are mainly used for maintenance of the IT ecosystem used in a company and ensures their functionality to support the desired goals of the business. However, ITOps is not a single type of operation. There are different types, for numerous purposes. The current article analyzes three of the most advanced types of operations: MLOps, AIOps, and DataOps.

MLOps

Machine Learning Operations (MLOps), focuses on rapid deployment and continuous delivery of Machine Learning. For those that are related to the topic, MLOps copy the functionalities of Software Development Operations (DevOps), but includes features that are exclusive to Machine Learning.

In terms of functionality, MLOps is based on the following principles.

· Unification of release cycle, for Machine Learning and Software Application respectively.

· Automated testing through data validation, model testing, and model integration.

· Adaptation of Machine Learning technologies, techniques, and projects for agile principles.

· Support of Machine Learning models and datasets for CI/CD system applications.

· Minimal technical malfunctions and digital debts.

· Essential language, framework, platform, and infrastructure readability and analysis.

Overall, there is a variety of opinions about the use of MLOps. There are those who believe that DevOps contains all the necessary elements for data applications and solutions and those who are in favor of MLOps due to smart elements of Machine Learning. However, the most accurate aspect of the two is a combination of both. It is a fact that DevOps contains the majority of functionalities, but additional Machine Learning principles are required in order to be enabled.

Illustration of a typical MLOps

AIOps

Artificial Intelligence Operations (AIOps) is a mixture of Machine Learning, Big Data, Streaming Analytics, and ITOps management. Generally, AIOps has the potential of being a thorough and complete replacement of ITOps, due to its capability of covering computer systems such as Cloud Systems, Internet of Things models, and embedded environments.

Due to the combination of different technologies into AIOps, their functionality principles are the following.

· Depiction of historical performance and event data.

· Streaming of real-time operations.

· Depiction of system logs and metrics, as well as incident-related data, document-based data, and ticketing.

· Depiction of network and packet data.

· Capability of recognizing significant abnormalities, while producing specific event alerts.

· Potential solution creation, proactive reactions, and automated analysis, through Artificial Intelligence, on a real-time basis.

· Capability of ongoing development and learning, by utilizing previous analytics. Such a process leads to more efficient results and solution propositions for future tasks.

It is almost certain that AIOps will eventually replace traditional aspects of ITOps. Although for some businesses Artificial Intelligence is considered advanced for implementation into the company’s premises, it is predicted that it will transform network management as we know it, while offering far more capabilities than ITOps or MLOps.

Illustration of the AIOps process

DataOps

Data Operations (DataOps) is slightly different from the other two types of ITOps. It is highly focused on data analysis. To be exact, it is an agile, process-based methodology, designed for the development and delivery of analytics. Other than that, many consider it as a combination of DevOps with data engineering and data science in general.

Operationally DataOps are defined by the following aspects and principles.

· Designing, developing, and maintenance an application based on data and the corresponding data analytics.

· Rapid innovation and robust experimentation, while producing solutions and insights efficiently.

· Decreased appearance of errors during data analysis while offering high-quality analytics.

· Acts as communication diode among different fields of technologies, environments, and people.

· Thorough results of monitoring, measurement, and transparency.

DataOps are considered the future regarding Data Management. More and more companies and businesses replace traditional methods of data management with automated practices of analytics and visualization. Scientists predict the success of DataOps over data optimization. Even further with the implementation of Machine Learning and Artificial Intelligence.

Illustration of DataOps lifecycle

Summary

In the current article, I introduced the basis of the topic through the ITOps. There numerous types of such operation, but we focused on the most advanced ones and potentially developed. For each type of operation, a definition is presented, as well as their functionality principles. Scientific opinion on their future is also included since they are promising technologies with the potential of offering technologically advanced solutions.

References

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