The Life Cycle of Machine Learning

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We all know that machine learning allows computer systems to work and learn automatically without human intervention. But still, one question arises: how does machine learning work? To answer the same, I am going to make you aware of the working of machine learning through this article. There is a life cycle of machine learning, and the process focuses on building an efficient project. The main goal of the life cycle of machine learning is to find a solution to the problem. There are various major steps in the life cycle of machine learning, and they are:

  • Collection of data
  • Data preparation
  • Data wrangling
  • Analyse data
  • Train the model
  • Test the model
  • Deployment

What is machine learning?

It is a subset of artificial intelligence, mainly concerned with the study that allows computers to learn from past data without human intervention. It is one of the most exciting and widely used technologies that one would have ever come across. It is an essential skill for analysts, data scientists, and those who transform a colossal amount of raw data into trends and predictions. It also opens various career paths for youngsters and professionals.

In the life cycle of machine learning, it is essential to detect a problem and know its purpose because a good result depends on a better understanding of the problem. What happens to get a good result or a solution to a problem? We create a model. Creating a model requires training, but to train the model, data is key; therefore, the cycle starts with data collection.

1- Collection of data

This is the first step, and the main goal of this step is to detect and obtain all data-related problems. We collect data from various sources such as files, databases, the internet, or mobile devices. This step is considered the most important step in the life cycle because the quantity and quality of data decide the efficiency of the output or result. The more data, the more accuracy will be in the prediction. Some tasks come under this step.

  • Filtering various data sources
  • Gather data
  • Now integrate the data obtained from various sources

2- Data Preparation

Once we are done with data collection, we need to prepare it for further steps. This is the step where we put this data in a suitable place to prepare it for machine learning training.

The two divisions or processes of this step can be classified.

Data exploration– in this, we must understand the nature of the data we have to work with. We must understand various factors such as characteristics, format, and data quality. This is essential because a better understanding of data leads to an efficient and effective outcome, and in this, we find correlations, general trends, and outliers.

Data reprocessing– in this, we process the data for analysis.

3- Data Wrangling

It cleans the data and transforms raw data into a usable format. Selecting the variable to use and then transforming the data into a proper format to make it more suitable for analysis. Cleaning or data is necessary to address the quality issues. Sometimes it is optional that the data we have collected is always of our users, as some may not. There are multiple issues in the collected data in real-world applications, including-

  • Missing values
  • Duplicate data
  • Noise
  • Invalid data

It is essential to detect and solve the issue because it can negatively affect the quality of the outcome.

4- Data analysis

Now, this step includes the following steps

  • Selection of analytical techniques
  • Building models
  • Review the result

The main goal of this step is to build a model for machine learning to analyse the data using different techniques and review the outcome. There are various machine learning techniques, such as Classification, Regression, Cluster analysis, Association etc.; in this step, we start with determining the type of problem. Then we decide which techniques will be used to overcome the issue or to resolve it, and then we build the model using prepared data and evaluate the model. Further, we take the data and use machine learning algorithms to build the data.

5- Train the model

Now, we are ready to train the model with the help of our datasets. We can now train our model to improve its performance for better outcomes of the problem. In this process, we use machine learning algorithms. It is a prerequisite to train a model because it can understand the various patterns, rules and features.

6- Test Model

Once we train our model on a given dataset, we test the model we have trained. In this step, we check the model’s accuracy by providing a test dataset. Model testing determines the percentage of accuracy in the model as per the requirement of the project or problem.

7- Deployment

This is the final step in the life cycle of machine learning, where we apply the model to real-world systems. We can deploy the model in the entire system if it provides accuracy according to our requirements and with an acceptable speed. It is also necessary to check

whether it is improving its performance using available data or not.


The above article is all about the life cycle of machine learning training, and these are essential to achieve the required goals. Above mentioned steps are an in-depth picture of the stages of machine learning. To meet the required solution of a problem or project, you must know the steps to take in the development process. It is used widely across the globe in different industries to make machinery automatic so that the productivity of an organisation can be enhanced. Apart from this, it also opens a wide range of career opportunities for many who want to explore themselves in this field. One who wants to make a career in this needs proper machine learning training, and for that, you can visit the APTRON institute in Noida.

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