Understand MLOps: Why It Matters and How to Get It Done

MLOps has been one of the most revolutionary discoveries in building machine learning models. What is MLOps? It is a methodology and a process of building machine learning models in the fastest and most efficient way possible. MLOps best practices have been adopted in the overwhelming majority of machine learning initiatives. It is essentially the model management system that makes bringing your machine learning efforts to reality.

If you are looking to get the fruits of your labor in terms of success in this industry, you need to be smart in understanding how MLOps works. Developing a machine learning algorithm has often resulted in failure for most companies attempting to do so. However, using MLOps has significantly resulted in far more initiatives reaching success than previously. We are now getting to a level of productivity in the industry that we could not fathom only a decade ago.

Comparing MLOps to DevOps

What is MLOps, and how does it compare to DevOps. Many people often compare DevOps and MLOps. However, MLOps is different enough that it warrants understanding how they differ. The MLOps definition you need to know has to do with how it relates to managing your model and your data. You need version control systems to do both instead of just your source code.

There are also different ways that an MLOps platform makes things work as well. Another thing to think about is the fact that MLOps is more experimental than DevOps. In DevOps, companies already know what they’re doing to get the code to production. The testing you do is about validating the code you have. However, machine learning involves testing and experimenting to get the results that companies look for.

Components of MLOps

The various components of MLOps have to do with the various steps involved in building a machine learning model. Most things can be automated with an ML platform, but not many people are using these right now. The main thing to note is that it all begins with exploratory data analysis and feature engineering. This is where the first two components of the puzzle start to make sense.

You also have the model training, tuning, and governance side of MLOps as well. Once that has been completed, you also have model retraining and eventually deployment and modeling. Once those are completed, you get to the model inference and serving steps. All of these make up the various components needed in MLOps operationalizing data science systems.

Choosing a Great MLOps Platform

The machine learning industry has gotten to a place where there are now great MLOps platforms like xpresso.ai. This is great because it allows you to do MLOps best practices without even thinking about it. You also get a model management system that makes it easy to control various versions of your system.

This system makes it possible to get excellent results in your modeling initiatives. It also makes it a lot easier for you to bring machine learning models to production with an ML platform that is fun and intuitive to use. With a great system like this, you are able to integrate everything you need into one place relatively quickly. So, to finish up, What is MLOps, and why does it matter? The answer is in the results Machine Learning companies are currently getting.