One of the biggest problems with machine learning projects is that most of them never make it into production. In fact, 87% of these projects, to be precise. The main reason is that there are many engineering and data problems that need to be overcome for ML teams to build a successful project.
In fact, most ML teams spend most of their time working to build the infrastructure needed to start deploying ML models. This is obviously a bad thing because they waste a lot of time doing something that doesn’t necessarily benefit them. It is for this reason why MLOps exists
What Is MLOps
What is MLOps? It is a set of best practices and tooling that enable companies to streamline machine learning operations. It can be thought of as DevOps for machine learning projects. Obviously, machine learning operations are a lot more complicated than software projects. They involve more than just software code.
You also have multiple different professions working on the same projects, which itself adds a lot of complexity. On top of that, your business executives might not be on the same page as your data team. MLOps solves all these problems by providing the best practices that enable companies to streamline operations and create successful machine learning projects. These best practices also help companies create a streamlined ML pipeline to continuously deliver excellent models to production.
The Innovations Made by MLOps
Machine learning projects are fundamentally different from software ones. In machine learning projects, you have both software code and data. From this data, you need to build complicated ML models. You end up with a CI CD CT DevOps process that isn’t comparable to anything else. The CT stands for continuous training.
The majority of models are not perfect the first time they are deployed. They need continuous training and evaluation to ensure that they meet your business needs. It is one of the many reasons why MLOps is still so complicated. You must develop code and data in parallel, and they can never fall out of sync. You also must have different teams with different skill sets working together on these projects. It makes things a lot more complicated, but it is a lot more efficient than the alternative ways of deploying machine learning models in production.
Understanding the Full ML Pipeline
The full ML pipeline is much more difficult than the DevOps pipeline, but that is due to how complicated ML products can be. Before you can even start anything, you have to look at the business benefits of deploying machine learning to production. You have to then build the data source and training pipeline that is needed to be successful. It means collecting and preparing the data to be used inside your model.
You are also required to train the data, but this is a continuous process. Most companies never get to the end result they want after the first model. You have to continuously evaluate and improve the model as time goes on. You can think of MLOps as a cyclical process that is more complicated than DevOps.
On top of all of this, you also have to build the right team with the various professions needed to come together to make this process work. However, MLOps is a radical shift in the way people build and deploy ML models. It has proven itself to be quite useful, and it will continue to do so for the foreseeable future.