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MLOps vs DataOps: Key Similarities & Differences in 2024

Cem Dilmegani
Updated on Jan 12
2 min read

DevOps practices were born to enable the software development and operations teams to work together efficiently. It has accelerated many processes in the software development field. While DevOps minimizes miscommunications between teams, it also uses automation tools to decrease the number of repetitive tasks to allocate more time for more strategic decisions.

Inspired by DevOps practices, new practices such as MLOps and DataOps have evolved to keep database operations and machine learning operations running smoothly.

In this article, we will explore MLOps and DataOps methods and discuss their similarities and differences.

What is MLOps?

MLOps combines operations with machine learning. It automates and streamlines the entire ML lifecycle, from production to development, deployment to retraining, encompassing DevOps practices such as Continuous Integration (CI) and Continuous Deployment (CD) for efficient model management.

In addition to CI/CD, it adds the continuous training (CT) principle to enable systematic model monitoring and model retraining.

What is DataOps? 

DataOps is a process-oriented methodology used by data teams to improve the quality of data, increase the efficiency of analytics, and reduce the time cycle of data analytics. DataOps takes DevOps practices and integrates them into the data management workflows. 

DataOps automates processes such as visualization and reporting by creating a pipeline with data security, data quality, and data engineering stages. Thus, DataOps improves the availability, accessibility, and integration of data.

This methodology powers data pipelines and machine learning models to help companies extract value from their data. DataOps is used by data architects, data engineers, data analysts, and data scientists. 

Figure 1: DataOps process

Source: Alexsoft

In short, DataOps helps businesses to:

  • Create automated data pipelines
  • Centralize data and break down data silos
  • Democratize data by making it available to all stakeholders

Similarities and Differences between MLOps and DataOps

Figure 2: Relationship between DataOps, MLOps, and AIOps

Sources: Unraveldata

Both MLOps and DataOps involve:

  • Collaboration for workflow: The operating philosophy of DataOps and MLOps is to achieve harmony and speed by encouraging different departments to work together.
  • Automation: Both of them work towards automating all processes in their pipelines. DataOps automates the entire process from data preparation to reporting, and MLOps automates the entire process from model creation to deployment and monitoring.
  • Standardization: While DataOps standardize the data pipelines for all stakeholders, MLOps standardize the ML workflows and create a common language for all stakeholders.

The main differences between MLOps and DataOps are:

  • They deal with a different set of questions and objectives in the machine learning lifecycle and require different types of expertise and tools.
  • You can have DataOps without MLOps because you can have data extraction and transformation without machine learning. The contrary is barely true.
  • DataOps is applicable across the complete lifecycle of data applications. MLOps is primarily for simplification of management and deployment of machine learning models.
  • The goal of DataOps is to streamline the data management cycles, achieve a faster time to market, and produce high-quality outputs. The aim of MLOps is to facilitate the deployment of ML models in production environments.

If you want to get started with MLOps in your business, you can check our data-driven list of MLOps platforms. If you have other questions, we can help:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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