MLOps vs DataOps: Key Similarities & Differences in 2024

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

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

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: