Is MLOps Another Redundant Terminology? - Analytics Vidhya

This article was published as a part of theData Science Blogathon.

MLOps? Many persons have barely finished digesting the meaning of DevOps, and here come a new term, MLOps. But, those who understand the mhat MLOps is, but you know the meaning of machine learning and DevOps, then just join the two meanings together, and you are good to go. People who will have issues understanding the term MLOps will be those who do not initially grasp the concept of DevOps. Here in this article, I will walk through the idea of MLOps so that you won’t necessarily need the knowledge of DevOps.

Sincerely sometimes, I think the terminologies in tech are becoming too much. Maybe it is because the sphere of tech is artificial, so things easily multiply and get complex compared to the natural world, where even a little mutation must take billions of years. Sometimes I feel maybe if all the terminologies are collected and organized, some will not be necessary as they will be redundant. Tech is a market, so every brand is trying to be unique, causing a swamp of terminologies.

MLOps is a short term for Machine Learning Operations. Just see it literally. It is simply the activities (operations) involved in machine learning, except it is carefully designed to meet industry standards more efficiently. It creates a standard method for developing ML models from start to finish to deployment and maintenance. The core reason for MLOps is integration. To not just build models but have a collaboration between ML practitioners and the general tech world.

This union between ML practice and the outside world becomes a very useful approach for creating machine learning and AI solutions. It also now allows data scientists and machine learning engineers to collaborate and improve the way new models are developed and old ones are maintained.

The guidelines and best practices presented in MLOps help provide an atmosphere that improves a usual machine learning life cycle systematically. Since ML is done to improve human life, there has to be a way to introduce it into various works of life. This is where ML engineers shake hands with IT and various other tech fields. It is just a merging of DevOps and ML.

We can also see MLOps as a division of labor. Instead of the ML engineer worrying about building models from start to deployment and even maintenance and industry demands, he just focuses on ML. He joins hands with ‘Ops’ or the usual DevOps system.

We can see Machine Learning systems in group activities, including data collection, processing, feature engineering, labeling, model design, training and optimization, deployment, and maintenance. Just the last two stages alone are where the model finally leaves the experimental environment and touches the purpose it was started. Instead of allowing the ML engineer to do it alone, MLOps assists from here to provide smoother transition and maintenance. Imagine the ML engineer also bothering on version control? MLOps ease it all and shorten the time of development.

MLOps covers various disciplines accordingly, assisting enterprises to have an efficient workflow and avoiding many challenges that come with development. According to research, only a few ML companies have used it effectively.

Machine learning models are striving in a very dynamic space. Variables take many possibilities and forms. The data coming in is continuous and needs more attention than hard coding operations. Although this, comparably little attention has been given to the practice of MLOps. Most beginners are still yet to start learning the concepts, and experts are mostly still trying to adapt, making it a challenge to strengthen this new standard.

The chief benefits of MLOps are simply developing more efficient, scalable, and controlled ML systems. The promises of ML become easily realistic in this standardized operation. It has created a pipeline that fosters data teams to save development time while achieving high-performing systems. Continuous testing, for instance, even deployed systems can be scaled to automatically meet new demands. Let’s try to pinpoint some more benefits of MLOps.

Version control is a usual activity in software systems. The data and models developed will also require versioning in this area too. Introducing new data to the system after a model is deployed could also be beautifully versioned as it is either used as new data or merged with the history. This is can be done via metadata and other means. For some systems merging the old data may make sense and in some, it may not. In both cases, MLOps will provide a way out.

Instead of allowing the ML engineer to work separately and the other integration space to work separately, the two can work more organized. This prevents many problems and enhances efficiency.

The scientist now has a bigger team to verify and validate the results of models before they are deployed, when they are deployed, and after they are deployed. Validation doesn’t only figure out the truth. It can also bear new possibilities. Even the data presented to ML engineers can properly be validated by the MLOps.

Let us see the machine learning pipeline from this point and how MLOps comes in. MLOps sees ML more from the user end how it can be used best in production.

This involves the first step of ML. The data has to be carefully recognized, not just as data but also based on the scope or problem domain. Apart from just the usual benefit of this step in ML, this can be further improved to save other issues like avoiding data swamps. Though lots of data is required for ML, we now handle this need carefully and avoid other challenges.

In verification, we check if the data is complete, in a good format, organized, clean, etc.; in validation, we still confirm if it meets the needs of the outside world. These are distinctly important to avoid waste of time.

Instead of just consuming, we can select the features that best fit the goals. Not everything may be needed for the final prediction, clustering, or optimization. Here you may discover the need to clean the data further.

A robust system, which is the goal of MLOps, should have efficient communication between its subsystems and external systems.

The codes should also follow best practices. Instead of just coding as a natural ML model, we consider other arts that will also support the goal MLOps. The coding way should facilitate good documentation and automatic testing and integration.

here we follow a process of ensuring the ML pipeline achieves all project goals within the given constraints. This is done through project documentation and all other report writings created at the beginning of the development process and throughout the project lifecycle.

Lastly is the monitoring phase. The model is attached with a subsystem for monitoring to ensure enough platform is set to oversee the deployed model and the domain it serves. Below is an instance of a dataset monitor called Shapash.

We have been able to see the MLOps concepts from a clear perspective. With all the benefits presented above, do you think MLOps is just another redundant tech terminology? Though it’s important may not quickly enter your mind at first thought, but by now, you should have been able to see that MLOps has come to stay and will very likely become more advanced than the initial idea of DevOps. There is a statement that ModelOps is the main category while MLOps is a subset. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models. AIOps is also another so-called ‘Ops’ case of systems in Operations. So many Ops have arisen recently, and many more will still come up.

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