A Detailed Look Into Machine Learning Model Development and Operations

There are few things more difficult than developing machine learning models. Putting an ML model into production is a multistep process, and you need to understand every piece of it to be successful. Many companies don’t know what it takes to develop machine learning models, so they end up having problems. Understanding the ML model lifecycle is crucial for you in many other areas as well. If you are good at this lifecycle, you will be able to develop machine learning models and improve your business results.

The first thing you need to do when building machine learning models is to start with the data acquisition phase. After that, you can start with machine learning model training and machine learning model development. The ML model lifecycle involves taking that data and building a model that can predict things in the future. If you do it well, it becomes a really huge part of your application.

The Model Development Process

The entire ML model lifecycle is quite remarkable. The first step is sourcing the data. If you don’t have proper data, you might need to generate that data by yourself. The way we develop machine learning models isn’t as complicated as you think when you put it into this context. Model development becomes easy because you can then focus on tuning your model based on the data you have. However, it is important to remember that you are putting this model into production.

You have to think about how you will put it into production during this stage of model development. The exploratory data analysis stage is quite important because it will show you whether it is worthwhile to develop this model or not. Sometimes, people find out that it does not make any sense to have an ML model built during this phase.

Data Analysis and Feature Engineering

After you have decided to start building your ML model, the next stage of the ML model lifecycle is exploratory data analysis and feature engineering. Unfortunately, exploratory data analysis usually takes up to 50% of the time needed to develop a model. This is where you find the data and process it down into the features needed to build your ML model.

The model development process isn’t as complicated as it seems, but this is a very difficult step for most people. Feature engineering is essentially the process of building the variables that your ML model will understand. This is how it works when you do machine learning model training. You are then able to adjust the parameters that the features give you.

Model Deployment

The last stage is often the most difficult and least understood. Every ML model is useless until it is put into production. This is why you need to understand the machine learning model development process at this phase. If you don’t, you will be one of the many companies that fail to enact machine learning into the application.

You need to understand MLOps because this is a crucial step in managing your ML model. It takes special skills to develop machine learning models, but the deployment step is typically where those skills are tested to the maximum. If you know what you’re doing, you can often then get to the next stage of model development without any issues.