How Machine Learning Model Management Works
It is probably a well-known fact now that the majority of machine learning initiatives fail. The reason they fail is that the process of going from an idea to a finished model is extremely difficult. Because of this, projects often get abandoned during the process. On top of it, most companies don’t even have a process in place to do machine learning model management effectively. The purpose of every machine learning project is to put a good machine learning model into a production application. However, deploying machine learning models in production is one of the most difficult concepts in the industry.
What Machine Learning Model Management Is
Every machine learning project ends with a model. The machine learning model is the thing you are trying to produce. It is what is deployed with your production application, and it helps to predict the correct results. However, you don’t always end up with one good machine learning model. That is why model management is so crucial. Model management is vital because you often end up creating multiple models that need to be organized systematically.
That systematic way is usually the purview of MLOps, but there aren’t many tools that have grown in popularity to do it yet. You also have to deal with the outside factors that influence whether your machine learning project will be successful or not. For example, your data sources might change, leading to problems with the accuracy of your models. You might also have a change in the way the business is run, meaning you might have to develop a better machine learning model. Either way, you need a way of managing the multiple iterations of your model that you will have to go through to build a successful machine learning project.
The Continuous Nature of Machine Learning Model Management
The reality is that deploying machine learning models in production is not a linear process. This reality introduces some challenges for companies trying to get into machine learning model development and management. There are extra steps that need to be taken to ensure that things don’t go wrong. One of those has to do with the lifecycle management you go through when doing machine learning model management. You also require MLOps skills to automate a lot of the process. That is because doing it by hand would be too time-consuming. On top of that, you need skilled people to implement whatever model management scheme you have going for you. This is ultimately where many companies fall down.
MLOps As The Solution
One of the solutions to the chaos of machine learning model management is MLOps. It is an entire workflow that is dedicated to automating many aspects of the machine learning model development process. It makes it easy for you to streamline your workflow and improve your operations. One of the best things about MLOps is that there are solutions like xpresso.ai that act as an all-in-one platform to enable your machine learning workflows. It can help you with model management, which will improve your ability to bring machine learning programs to the marketplace.