The field of Artificial Intelligence (AI) has gained rapid momentum in these recent years. Today data science and machine learning models have the potential to solve complex business problems.
As a result, businesses are investing in this field to deliver business value to their users. Nowadays, two buzzwords have captured the attention of the software development industry- DevOps and MLOps.
Many companies found DevOps as an essential tool to scale the software development industry worldwide.
According to the survey, almost 80 percent of respondents believed that DevOps is somewhat important, with nearly half of them claiming it is imperative. Due to the booming need for fast application delivery with high quality, the demand for DevOps solutions and services among enterprises has gained massive traction. On the other end, MLOps is popular to drive business success. It is a perfect blend of “Machine Learning” and “the continuous development practice of DevOps in the software field.”
MLOps and DevOps are the hot topics of discussion among the software development professionals as both provide a competitive edge to the businesses.
Though both methodologies have a powerful impact and are somehow interconnected, certain factors separate them. This post outlines the main concepts and potential benefits of MLOps and DevOps and explains how both differ from each other.
So, let’s start with a quick overview-
What is DevOps?
DevOps is a combination of tools and practices designed to increase the organization’s capabilities to deliver faster and secure application development than standard software development processes.
It evolves and improves products faster and helps businesses enhance their customer service and establish a robust image in the competitive market. Speed, Security, and Reliability are the best components defining DevOps demand and need in software development.
In simple terms, DevOps is not a technology, process, or standard; it is a complete culture of philosophies, practices, and tools that companies use to enhance their business abilities and deliver value to their end-users.
“Development + Operations = DevOps”
Benefits of DevOps:
DevOps not only provide benefits in the business process but also helps you with the technical and cultural benefits-
Business Benefits of DevOps-
- Rapid application development with robust features
- Stable operations environments
- Improved communication and collaboration
- Reliability
- Provide space for innovations.
Cultural Benefits of DevOps-
- Increases employee engagement
- Enhance Productivity.
- Increase level of trust among teams.
- Creates efficiency and reduces the rates of failures and errors.
Technical Benefits of DevOps-
- Process Automation
- Convenient Security Maintenance & Agility
- Documentation & Code Synchronicity
- Continuous Integration and Delivery
- Faster resolution of issues
- Dynamic Iteration Cycle
What are MLOps?
If you wish to run AI successfully in software products and cloud services for your business, MLOps are the best practices for it. MLOps stands for Machine Learning Operations, a set of practices for collaboration and communication between data scientists and operations engineers. It is generally the idea of combining the long-established practice of DevOps with the emerging field of Machine Learning.
MLOps is a new idea in the AI world that explains how to manage the data scientists and operations professionals best and enables effective development, deployment, and monitoring of models.
“Machine Learning + Continuous development practice of DevOps = MLOps”
Benefits of MLOps:
- Reduced time to market AI-driven products.
- Rapid innovation through robust machine learning lifecycle management
- Creation of reproducible workflows and models
- Improved traceability by tracking code, data, and metrics in the execution log.
- Smooth deployment of high precision models in any location.
- Enhanced user experience with quick application updates.
So, after a quick overview of DevOps and MLOps, you might find some similarities between these two as both are considered a proper culture, not a process or technology. This is because MLOps have taken many principles from DevOps.
The two crucial similarities between MLOps and DevOps is-
- Both facilitate communication and collaboration between people like engineers, data scientists, and other stakeholders.
- Both emphasize maximizing the speed and efficiency, keeping process automation in continuous development.
Now let’s talk about their differences-
Difference between DevOps and MLOps:
1. Adaptation for Machine Learning- To ensure clear documentation related to any changes or modifications in the software being developed, DevOps utilizes code version control. With Machine Learning, apart from the code changing, data is another vital input required to be managed with parameters, metadata, logs, and the model.
2. Agility- DevOps has CI (Continuous Integration) and CD (Continuous Delivery) as its best practices to implement. MLOps comes with the third concept known as CT (Continuous Training), which is all about automatically identifying the events, which requires a model to be re-trained or re-deployed into production. Retraining or redeploying a model is needed due to the performance degradation in the current machine learning model or system.
3. Performance- Performance degradation of the system requires quick attention. In ML and DL, the continuous evolution of the data profiles affects the performance, which is not common in traditional development. Hence every time the models need to be refreshed even if it is active currently. This leads to more iterations in the pipeline.
4. Training- If a model performance is dropping, it must be retrained with new and validated data before rolling out production again. Thus, Continuous Training (CT) and validation in MLOps take over the Continuous testing practice of DevOps.
Wrap Up-
In a nutshell, both DevOps and MLOps play a vital role in the software development industry. On one end, where DevOps increases the client’s satisfaction and improves customer services, the other side, MLOps helps in managing the development and productization of machine learning models. So, we hope now you get an understanding of what DevOps and MLOps are and their roles.
Let us know if you have any questions or require any advice; we would love to hear from you.
FAQ:
What are the key benefits of DevOps?
If you wish for application development with shorter development cycles, high deployment frequency, and less complexity, DevOps is the best practice to go with.
How do you ensure the quality of my project?
We have a dedicated team of DevOps and Machine Learning developers that perform variously automated and manual testing at every level to keep the project solution errors free.
What business benefits will I get after investing in DevOps and MLOps?
If you look for more stable operating environments and improved communication and collaboration among teams, you should go for DevOps and MLOps.
Author Bio:
Name: Himanshu Singh
Himanshu Singh is a Marketing consultant at Rapidops. He is a technology enthusiast and well versed in software development. He is also interested in domains like machine learning and data science. In his spare time, he enjoys guitar, badminton, and photography.
