About applications and development, there are two distinct approaches to operational processes - those of MLOps and DevOps. MLOps (model of operation) involves a model of the business and a more or less delineated set of organizational goals and objectives. In essence, this is more of a generic, functional approach to business that attempts to address issues in product development more than in manufacturing. The primary difference between ML Ops and DevOps is that MLOps relies more on programming language abstractions, such as code abstraction or DSLs, whereas DevOps places a greater emphasis on the relationship between business logic and hardware abstraction. In addition, they share some developers' views about the need to support advanced platforms and technologies such as Java.
Both MLOps and DevOps aim for model performance and deployment. By "model", we mean both the execution and result of the application or series of applications developed. The key difference between the two is that MLOps tries to achieve a model of production using deployment. In contrast, DevOps tries to enforce a model of development. While this is possible using traditional programming languages (such as Java), most developers do not feel comfortable writing such code.
MLOps vs DevOps can be seen from a different angle. The biggest difference between the two is that MLOps assumes that all processes are independent, while DevOps assumes that a consistent model of production and deployment is necessary. This leads to several problems for developers who maintain a consistent model throughout their projects. For example, in a complex application, it might be necessary to configure various deployment components such as servers, databases, and containers. Therefore, in these cases, it would make sense for developers to use a programming language that allows them to express the process of creating a container, then deploying it independently from other components.
When comparing MLOps vs DevOps, one must also consider the differences in engineering practices and priorities. MLOps primarily focus on quality through good quality engineering practices, which lead to a reduction in costs without a corresponding reduction in inefficiency. On the other hand, DevOps focuses on establishing a model of production through careful testing of code, rather than quality improvement. In addition, both DevOps and MLOps require a significant amount of expertise, and even the most skilled engineer will not be able to completely alleviate the cost of deployment. As such, while the primary focus of the two processes is to control the cost of production, they have diverged in the area of engineering practice.
Both MLOps and DevOps share a commitment to model checking and model serving. However, unlike MLVs, which look primarily to reduce cycle times, DevOps requires a significant amount of expertise to ensure that a model runs smoothly. In addition, while it has a focus on quality, there is no commitment to using the best available technology. Thus, while a model can be implemented using available technologies, it would make little sense to do so if those technologies did not meet the requirements of the project. The result is that in a business process, an MLOS may appear as a deviation from the original business process, while a Dev Ops approach would appear more conservative and agile.
While an MLOps team might consider changing to an AIO or DIEP based on the relative costs of implementation, it would ultimately be a deviation from the business focus. By contrast, the DIEP team's view is clearly defined by the need to reduce costs while maintaining or increasing operational productivity. When operating a distributed system, both an AIO and DIEP provide a means to achieve these ends by utilizing various techniques and approaches. However, for software testing, a more defined approach is preferable, especially when the business focus is on improving a specific process, such as warehouse inventory control. While an MLOPTS might work for some testing approaches, an AIO is clearly the preferred choice for these operations specialists. Check out this post for more details related to this article: https://en.wikipedia.org/wiki/Automated_machine_learning.