There are two major types of model-based software engineering, namely: MLOps vs DevOps. In this article, we'll explain how MLOps and Dev Ops differ and look at the pros and cons of each one. Both have their own benefits and drawbacks depending on your business requirements and reasons for choosing them over each other. We'll also compare and contrast MLOps and DevOps by looking at the business value of each model, as well as looking at how they differ from traditional software development.
The two different models in MLOps involve a high degree of model abstraction in the application, where an engineer writes a program once, runs the application in a Java virtual machine, and then uses a large amount of programming language to write the "shell" code that controls the machine. This is very attractive to data scientists, who like being able to model complex business problems using simple, easy-to-understand programming. However, this high level of abstraction also comes with several disadvantages. Discover more about DevOps now!
First, because an engineer can run the application in a Java virtual machine, the code is often written in a highly generic manner. This means that when it comes to complex problems, it's very difficult for a programmer to make a concrete solution, and so the resulting solutions are often weak and less useful. For example, if you are working on a large-scale manufacturing operation, using a generic piece of a software system to create production data and requirements can lead to all sorts of problems such as poor scalability, poor fault tolerance, and poor quality production. This can be avoided by using a formal model-based programming language such as MLOps.
Second, while a large amount of programming is involved in MLOps, there is an inherent problem with a model-based approach in terms of data preparation. Unlike data preparation in traditional software systems, data preparation in MLOps requires a tremendous amount of manual work. Furthermore, the data that must be presented to users can change over time, making it necessary to re-propose information over again. This leads to an "artistic" rather than a "formal" architecture. In other words, MLOps machines are designed to operate at higher levels of abstraction, and thus require the programmer to do less work in terms of establishing a clear vision of the user's requirements. Because MLOps machines are run on large workloads, this results in both superior overload protection and better throughput.
Finally, one of MLOps vs Devops' most important aspects involves model versions. Although MLOps machines can be designed using traditional programming techniques, model-based methods of software engineering (also sometimes called a model or domain-specific languages) tend to be more practical for large-scale operations. By training models on large workloads and providing end-user support, data scientists train models to work on a smaller version of the real workload.
When presented side-by-side, these three major differences between MLOps and DevOps make a compelling case for choosing MLOps over DBA. As data science tools, MLOps machines are much more flexible and intuitive than DBA machines. With a flexible architecture, they can adapt to changing needs in real-time and provide the support needed to keep business functions running smoothly. They are easy to train and install and allow for a broader range of applicability and flexibility across a wide variety of applications. Finally, DBA models are costly and largely unmanageable. If your business needs a more structured approach to data science, MLOps machines are a better choice.
For more details about this topic,read this article: https://en.wikipedia.org/wiki/DevOps.