Understanding and Implementing MLOps

MLOps is a new field that has been brought about by the complexity needed for model training, model validation, model monitoring, and other machine learning operations needed to put real products into production. The truth is that understanding MLOps is critical in machine learning because these principles, routines, and processes are an excellent way for you to get a clear understanding of what is going on.

The main thing to understand about MLOps is that it has to do with the iterative process that makes machine learning work. MLOps exists because of how complex it is to turn an idea into a working machine learning application. It is significantly more complicated than software engineering, which is why it needs a lot more than what DevOps can provide. MLOps builds on what DevOps does, but it provides more features that are particularly tuned for machine learning projects.

Introduction to MLOps

You can think of MLOps as a set of principles to help data scientists, machine learning engineers, and infrastructure engineers turn a hypothesis into a working model. It is an iterative process, as you don’t just build a model and move on. There are also things you need to account for, like model monitoring. Another major issue is that machine learning projects require different types of people compared to traditional software engineering. In software engineering, you only have programmers writing code. However, machine learning projects involve data scientists, machine learning engineers, and there are people doing the model validation and model training.

How MLOps Compares to DevOps

MLOps gets a lot of its inspiration from DevOps. However, there are fundamental differences that are worth understanding. In DevOps, you go from code creation to testing to deployment in one smooth fashion. However, machine learning projects are very different.

The first step involves getting the data to test out your hypothesis. Not only that, but you also have to clean that data and make it ready for model building. These machine learning operations are quite complicated, and they are not done just once.

In DevOps, you have version control systems that do a good job of ensuring that your code is well versioned and secure. However, in machine learning projects, you need to keep track of code, models, data, and associated metadata needed for model training, model validation, and even model monitoring.

The Process and Implementing It

There are multiple levels and layers in the process of implementing MLOps. However, an easy way to implement it is through an MLOps platform. This is an integrated platform with all the associated tools and functionality you need to build full-fledged machine learning models in a flash. These platforms incorporate the best practices needed, making it possible for you to focus on your business objectives and model.

These platforms also streamline the process of building out your ML pipeline and automating all aspects of it. The version control system is also great, as it means you can focus on what the data does instead of how clean it is. A system like xpresso.ai does all of this without you having the deep knowledge of MLOps needed to build your own system.