DataOps – AIOps – MLOps – Explained

Every trend in how IT operations are handled these days gets an “ops” byname such as DevOps, DevSecOps, AIOPS, DataOps, MLOPS and a few other like as GitOps and FinOps.

Earlier, it was common practice to segregate business functions from IT operations. But those practices are now a distant memory and for good reason. The Ops prospect has moved beyond the general “IT” to include DevOps, DataOps, AIOPs, MLOps, and more. Each of these ops practices are cross-functional across the organization, and each offers a unique advantage.

And each of the Ops areas arises from the same common mechanism – Applying agile methods and principles, originally created to guide software development, to the overlap of different flavors of software development, related technologies (data-driven applications, AI, and ML), and operations.

In this blog we are going to understand the overview of DataOps, AIOps and MLOps.

What is DataOps?

DataOps – DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. (Wikipedia)

DataOps is aimed directly at data operations teams, data engineers and software developers who build data-driven applications and the software-defined infrastructure that supports them. With massive data these days it’s really hard for teams to collect, clean, and analyze it to find insights that can help their businesses. This is where AIOps can save our lives, by helping DevOps and data operations teams choose what to automate, from development to production, this practices helps teams to evaluate and predict performance problems, do root cause and end to end analysis, find inconsistencies, and more.

What is MLOps?

MLOps – MLOps is a process for collaboration and communication between data scientists and operations professionals to help manage the production of ML lifecycle. It emphasize increasing automation and improve the quality of production ML while also focusing on business and regulatory requirements. (Wikipedia)

MLOps helps simplify the management, logistics, and deployment of machine learning models between operations teams and machine learning researchers. It is pretty similar to DataOps – the amalgamation of practices (machine learning in case of MLOps, data science in case of DatOps) and the operationalization of projects from that discipline.

What is AIOps?

AIOps – AIOps is an industry category for machine learning analytics technology that enhances IT operations analytics. Such operation tasks include automation, performance monitoring and event correlations among others. (Wikipedia)

AIOps abbreviation of Artificial Intelligence for IT Operations. It is a new methodology that enables machines to solve IT ops issues without the need for human intervention. It observes IT operations data intelligently in order to find the root causes and recommend solutions based on that observation quickly and it may be implemented without human interaction.

Hopefully, this short discussion on topics was interesting and will help you to understand DataOps AIOps – MLOps. If you are looking for guidance on these concepts training and certification – you may connect with course advisors on-call/WhatsApp +91 700 483 5930 |

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