DataOps vs. MLOps: What’s the Difference?

DataOps (data analytics operations) and MLOps (machine learning operations) are both inspired by DevOps, a process for managing software development projects that merges operations and development into one Agile-inspired process. This means that both DataOps and MLOps have the goal of increasing efficiency, collaboration, and organizing work in repeatable cycles. However, DataOps applies this process to data management, while MLOps applies this process to the machine learning development lifecycle.

The surge in artificial intelligence (AI) adoption has increased the need for efficient data management across industries. A survey by Enterprise Strategy Group found that 49 percent of organizations planned to increase their investments in DataOps, aiming to improve data access for their users [1]. Additionally, the MLOps market is expected to grow at a CAGR of 39.7 percent from 2023 to 2030 [2]. This means that understanding the difference between DataOps and MLOps is becoming increasingly more relevant if you work in data analytics or AI. Explore what applying the Agile principles that inspire DevOps to machine learning and data analytics looks like by comparing DataOps versus MLOps. 

What is the difference between DataOps and MLOps?

The main difference between DataOps and MLOps is the field in which they apply the principles of DevOps. DevOps, which stands for development/operations, is a method of developing software inspired by Agile that focuses on working in repeatable cycles to plan, build, test, and deploy code. After deploying code, the team can gather feedback from users or clients before beginning the planning stage for the next cycle of development. DevOps principles encourage a transparent process, communication and collaboration, and continuous improvement. 

The similarities between DataOps and MLOps relate back to that DevOps inspiration. Both methods prioritize principles like repeatable cycles of continuous implementation, improvement, and automation. DataOps applies these ideas to data analytics and managing data, while MLOps applies these principles to machine learning models. 

While the two processes take inspiration from the same idea, they are different in practice because of the unique care, considerations, and policies that each requires. The differences and similarities are further complicated because you will often use machine learning and data analytics together. But DataOps focuses on the best practices for collecting, storing, and using data to perform analytics, while MLOps considers the machine learning development process. 

What is DataOps?

DataOps is a series of best practices for data management. The DataOps process organizes the data management workflow into repeatable cycles to increase efficiency, introduce automation, and make continuous improvements. Manual data management can be a time-intensive process for businesses. DataOps helps companies shift from manual, repetitive data management to a more streamlined, Agile approach by automating processes. Additionally, DataOps aims to break down siloes between data professionals and stakeholders, fostering communication and access to high-quality, reliable data. In this way, the data management process remains efficient even as the volume of data continues to grow, ultimately creating business value from big data. 

What is the difference between DevOps and DataOps?

DataOps refers to data operations and builds on DevOps (development/operations). DevOps is a software development philosophy that focuses on increased communication and efficiency through Agile software development principles. DataOps translates those principles to the data management lifecycle to improve the process of collecting, storing, managing, and analyzing data. 

DataOps components and tools

In addition to the DevOps/Agile-inspired principles of improving speed and efficiency in iterative cycles, DataOps uses automation, data pipelines, and data orchestration to organize the data management workflow. 

• Automation: You can use automation to efficiently cycle through routine or mundane tasks, which makes data processing and analysis more efficient and accurate. Not only does automation speed up the data management process, but it also reduces human error. You can apply DataOps to automate test script management, test data management, data extraction, workflows, and more. 

• Data pipelines: A data pipeline is an end-to-end sequence of processes from collecting data through to analysis and delivery, including testing, maintaining, and documenting data. The data moves through the pipeline from the data source to the end user or database. DataOps enables rapid deployment of data pipelines, allowing the management of several data pipelines by combining data from multiple sources. In this way, DataOps automates the data pipeline to ensure it is reliable and generates valuable business insights. Examples of DataOps pipelines include batch processing pipelines, real-time processing pipelines, or streaming pipelines.

• Data orchestration: Data orchestration in DataOps is the automated coordination and management of several pipeline tasks and workflows to ensure that the data pipeline progresses efficiently and reliably. Orchestration is important when you are deriving your data from varied sources because it helps organize your data in a single source of truth to reduce errors and make sure your processes remain scalable. 

What is MLOps?

MLOps is the process for building, training, testing, and deploying machine learning models in repeatable cycles inspired by Agile development and DevOps to efficiently create accurate machine learning models. MLOps has a data management component because machine learning models need a vast amount of data for training purposes. Thus, MLOps principles seek to bridge the gap between data scientists and machine learning engineers in order to streamline the production of machine learning models. MLOps ensures that the model development process is well-documented and reproducible, ultimately facilitating faster model deployment and higher-quality models.

MLOps vs DevOps

DevOps is a software development philosophy that draws on Agile project management principles to streamline the software development process. MLOps (machine learning operations) is a philosophy for developing machine learning models by applying the DevOps (development/operations) principle of iterative development cycles focusing on increased efficiency, scale, and speed. 

MLOps components and tools

MLOps reframes the concepts of Agile and DevOps to be relevant to the machine learning model development lifecycle. A few components and ideas important to MLOps include exploratory data analysis, version control, CI/CD, continuous improvement, and automation. 

• Exploratory data analysis: Exploratory data analysis (EDA) is a process for observing the characteristics of data without any preconceptions of what the data is, helping you better understand how you can use the data. You can use EDA in the planning phases to determine what data you’ll need to train your model. This will generally involve visualizing the data to identify patterns, removing duplicate or incorrect data, and transforming raw data into relevant features.

• Version control: Version control is a method of using a central storage repository as the main source of code. Developers working with the code can isolate copies of the code to develop away from the main code. When the developer finishes the new code, they can integrate the new changes into the code repository. Version control offers many benefits, including the ability to revert to older versions of the code if something doesn’t work properly after the changes, test multiple models on different pipelines, and track changes made over time.

• CI/CD: Continuous integration/continuous deployment (CI/CD) is a process borrowed from software development, which, in MLOps, refers to continuously training, deploying, monitoring, and retraining models, as well as continuously testing and confirming the accuracy of your data. This ensures that code changes are validated so that errors are identified early and that automation reduces model deployment time and improves production efficiency.

• Continuous improvement: Continuous improvement is another component built into the MLOps lifecycle. Between cycles, you can gather feedback about how the model works or observe the model in action before assessing where you can improve and starting the planning stage of the next phase of model development. By fine-tuning model performance and incorporating improvements, you can ensure that models stay relevant and accurate. 

• Automation: Automation is important in MLOps because it can speed up the tedious, repeatable processes within machine learning model development to increase efficiency and standardize the steps of the process. 

DataOps vs. MLOPs

DataOps and MLOps are closely connected because they have similar goals, they are both inspired by DevOps and Agile development principles, and because you may use data analytics and machine learning together (machine learning to understand data and data to train machine learning models). The two share other similarities as well: both emphasize automation to reduce errors and improve efficiency, promote cross-team collaboration, and support iterative development.

However, the two ideas are different in practice because DataOps refers to managing the data analytics pipeline and data lifecycle, while MLOps focuses on building and training machine learning models. This means that if your primary goal involves streamlining the machine learning model lifecycle, scaling machine learning projects, and automating machine learning operations, you should choose MLOps. Alternately, if you want to enhance data quality, automate data pipelines, and optimize the data lifecycle, you should go for DataOps.

Explore DataOps and MLOps on Coursera

Both DataOps and MLOps are processes for organizing workflows inspired by DevOps and Agile software development principles. DataOps outlines a process for managing data pipelines in data analytics, while MLOps outlines a process for managing the machine learning model lifecycle. 

You can further explore the concepts of DataOps and MLOps on Coursera. For example, you can consider the IBM Machine Learning Professional Certificate or the IBM Data Warehouse Engineer Professional Certificate program.

In the IBM Machine Learning Professional Certificate, you have the opportunity to master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles.

In the IBM Data Warehouse Engineer Professional Certificate, you have the opportunity to understand how to design and populate data warehouses and analyze their data with business intelligence (BI) tools like Cognos Analytics.