There are many different ways of deploying machine learning models. Machine learning life cycle management has gotten a lot more attention in recent years, but there are still people who have not realized the benefits they get from MLOps. The problem with machine learning is that you don’t always deploy your project successfully the first time. You might need plenty of iterations before you get things right. Because of that, you have to understand and have a system in place to facilitate how your projects are undertaken.
A significant problem with deploying AI models is that most companies aren’t successful at doing it. AI and machine learning models are quite difficult to deploy because there is so much extra effort that goes into the process. The ones that get deployed don’t stand up well because the people doing the deployment don’t have a concrete plan in place. That concrete plan would ensure that you can continually improve your model to stay current with the data it is being fed. It means you don’t suffer problems that come from data or model drift.
A Standardized Machine Learning Life Cycle
MLOps is important because it is the crystallization of the standard machine learning model lifecycle. In the past, companies had to develop their own methodology for deploying and managing their machine learning models in production. This would often cause problems, leading to an intense focus on simplifying and automating significant processes. The standard model involves data gathering, training, implementation, and maintenance. These different pieces of the puzzle all have their own tools that must be understood and maintained. You also need to have an enhanced understanding of training and manipulating the data until you have reached the right results.
Benefits of MLOps
MLOps takes that standard model and turns it into something that has plenty of tools to help you automate all aspects. For example, a major piece of the puzzle was figuring out how to do version control of your code, model, and data. Machine learning models have iterations in the same way you would with software code.
A data version control system was created to facilitate this task. There are also various other model maintenance and monitoring tools to ensure that things perform as they should. These are the key features be implemented that make MLOps such a vital piece of the puzzle. These benefits are only getting more pronounced, as we are moving to a software world where almost everything involves some aspect of machine learning.
How It Works In Practice
In practice, MLOps has been integral in helping to improve model performance over time. The set of practices it brings to the table allows machine learning engineers and companies to unleash their creative potential in all facets of machine learning life cycle management. It makes the act of deploying machine learning models easy and affordable to everyone.
Machine learning projects are no longer the domain of multibillion-dollar companies, and they can be implemented by almost anyone. It is also automating a significant portion of the puzzle, making maintenance and governance easy.