It could be that you are asking What is MLOPS? What are ML ops? MLOPS is short for Machine Learningops, a group of practices that aims to deploy and sustain machine learning models both safely and reliably in manufacturing. The term is derived from the continuous improvement process of DevOps in the Information technology field. The aim is to improve the quality and performance of all information systems, to provide a better service for customers, and to ensure maximum productivity from existing and new personnel.
In short, MLOPS is a set of best practices for building robust technical systems. It is designed around five core tenets which are: data integrity and accountability, transparency, collaboration, and productivity. Data integrity and accountability refer to the policies and procedures governing the handling of sensitive data and ensuring that it is well protected from any misuse. Transparency refers to the ability of end-users to analyze and therefore control the system; it also refers to the ability of stakeholders to receive updates in real-time and at ease. Productivity is the ability of teams to build and operate on a shared understanding of business objectives.
Data integrity and accountability thus refer to the systems and processes used by MLOps teams to build and operate on models which are consistently built to meet the business processes. This is unlike traditional management approaches, whose focus is on process innovation and productivity gains through the use of new, technically superior processes and models. Transparency, on the other hand, refers to the ability of end-users to examine the integrity and performance of the systems and pipelines. Collaboration and productivity are key aspects of MLOps, which refers to the sharing of ideas and decisions between teams. These can range from the sharing of operational workflows and requirements to more complex collaborating on designs and engineering solutions.
With a focus on efficiency, flexibility, and reusability, MLOps differs from its traditional predecessor in the sense that it has been designed for speed. There is no longer a need to abandon the work once it is started. All that needs to be done is to configure the necessary infrastructure and then continue with the usual workflow. All that remains for IT departments to do is upgrade or add to the existing models when necessary, and deploy them by the company's deployment requirements. The primary advantage of this approach is that it results in a smaller amount of expenditure and energy.
Building an IT network, storage, and transport facilities for an MLOps lab can be extremely complex. Traditional engineering design processes have demanded large investments in IT infrastructure and skilled IT professionals, as well as large infrastructures such as data centers and servers. These infrastructure investments require substantial upfront costs and years of maintenance for the machines to pay for themselves. The time taken to train new personnel and establish the needed infrastructure can put a serious damper on MLOPS initiatives. An MLOPS lab is built using already existing IT infrastructure that is procured through a flexible financing and procurement process.
Although the initial start-up cost may be higher than other options, the benefits to operational costs and efficiency are far more profound. In a traditional laboratory, data scientists spend most of their time analyzing large quantities of potentially unusable data. In MLOPS, engineers can focus their attention on providing quality service instead of chasing down bugs. By the time the lab finally moves to the end of life, its investment in technology, staff, and machines has paid for itself, leaving little room for maintenance or other costly mistakes.
You can read the following article to get more informed about the topic: https://en.wikipedia.org/wiki/MLOps.