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What Are the Stages of Machine Learning Lifecycle?

Published by Tigran Hovsepyan at May 2, 2022

The process of building and employing a machine learning (ML) model takes a lot of planning and hard work. The machine learning life cycle can be divided into five main stages, all of which carry equally important considerations. A thorough understanding of this life cycle can help data scientists manage their resources and get real-time knowledge of where they stand in the process. The five stages we will discuss in this article include planning, preparing the data, building the model, deploying it, and monitoring. 

What Is a Model Development Life Cycle?

Machine learning

The machine learning life cycle involves utilizing artificial intelligence (AI) and machine learning (ML) to build an effective machine learning project. It starts from the initial conception of a given project, moves to the development of the model, and ends with monitoring and optimizing its performance.

The end goal of the life cycle is to find a solution to a given problem by deploying an ML model. Like other models, a machine learning model can also degrade over time and needs constant maintenance. Thus, a model’s life cycle doesn’t end after deployment. Optimization and maintenance are vital elements to ensure that the model runs smoothly and doesn’t veer toward any bias.  

Why Is a Framework Important?

The machine learning life cycle is a framework that data scientists follow to build models from scratch for everyday use. Establishing a detailed framework for model development is essential for several reasons: 

  • It highlights the role of every person involved in data analytics initiatives
  • It serves as a guideline for building a fully-functioning model from inception to completion
  • It stimulates scientists and developers to work more meticulously and deliver top-notch results
  • It helps others to understand how a given problem was approached in order to modify or rework old models

Five Stages of ML Development Life Cycle

That said, let’s have a detailed look at the five major stages of the ML life cycle.

1. Planning

Every model development initiative should start with detailed planning by defining the problems you want to solve. Model building is a resource-intensive process, and you wouldn’t want to spend your time and money on problems that can be solved in easier ways. 

  • The first step is to clearly define the problem you want to solve, such as a low customer conversion rate or a high number of fraudulent activities.
  • Next comes outlining the objectives you would like to achieve by solving the problem. For example, possible goals could include improving the customer conversion rate or reducing the amount of fraudulent behavior. 
  • Finally, establish metrics for measuring success. What will be the accuracy rate of the predictions to be considered successful? Generally, an accuracy rate of 70% is already considered a great achievement, while a rate between 70% and 90% is thought to be ideal.

2. Data Preparation

The second stage focuses on acquiring and polishing your data. You’re most probably going to deal with a large amount of data, so you need to make sure that it’s accurate and relevant to start building the model. 

This stage is divided into several steps.

Data Collection and Labeling

Machine learning

Collecting a large amount of data may be pretty costly and time-consuming, so first, try to see if you can obtain data that is already available. If you find data from several sources, you also need to merge them into a single table. However, you can also collect data yourself through multiple channels like surveys, interviews, and observations.

Data labeling refers to adding distinctive labels to raw data, such as images, videos, or text. It helps categorize your data and separate them into particular classes for easier identification in the future. 

Data Cleaning

The larger your dataset, the more thoroughly your data will need to be cleaned. This is because all large datasets typically include multiple missing values or irrelevant information. Removing these before building the model will help increase the accuracy of the eventual model and reduce the chances of error and bias. 

Exploratory Data Analysis (EDA)

Before starting to build the model, the last critical step is to conduct data exploration. This approach analyzes the data and presents a summary, typically using visuals. Data exploration provides a sneak peek into the common patterns and helps data scientists to understand the dataset better before modeling.

3. Model Development

Once you have the data prepared, it’s time to develop the model. Model preparation is at the core of the machine learning life cycle, and it involves three subpoints: 

  • Model Selection and Assessment. The first step is selecting the type of model to be used for development. Data scientists usually fit and test different models to see which one performs better. Typically, they choose the model (classification model, regression model, etc.) based on the type of data they have and the one that has the highest accuracy rate. 
  • Model Training. In this phase, data scientists start to do experiments with the model. They input the data into an algorithm to extract outputs. In this step, the first signs of the final output are visible, which also helps to modify the model accordingly to assign better predictions. 
  • Model Evaluation. After the model is done training, the final stage includes evaluating metrics like accuracy and precision to measure the model’s performance. It also includes an in-depth analysis of the errors and biases. This allows analysts to come up with solutions to eliminate them. If needed, data scientists re-run the model after making the necessary improvements to improve accuracy and performance.  

4. Deployment

Model deployment is the stage where you integrate the model into an existing production environment to make informed business decisions. Model deployment is one of the most challenging stages of the machine learning life cycle. The IT systems of many organizations are still unable to recognize traditional model-building languages, so data scientists usually have to recode the models so that the production systems can understand them. As a result, this stage usually assumes a collaborative effort between data scientists and development (DevOps) teams. 

5. Monitoring and Optimization

Finally, it’s crucial to run constant maintenance checks and optimize the model periodically. The model may degrade over time, and to ensure that it continues to provide accurate predictions, software engineers need to monitor the model with the help of predictive analytics software and check for such issues as model drift or bias. 

Predictive analytics software uses data to identify current trends and best practices in any industry. For example, predictive analytics can forecast customers that are likely to churn or send marketing campaigns to those who might be interested. 


To sum up, the machine learning life cycle is a standard framework that data scientists can follow to gain a deeper knowledge of machine learning model development. Management of the ML model life cycle is usually conducted around this framework, which includes everything starting from defining the problems and ending with the model’s optimization. 

Tigran Hovsepyan

Tigran Hovsepyan

Staff WriterTigran Hovsepyan is an experienced content writer with a background in Business and Economics. He focuses on IT management, finance, and e-commerce. He also enjoys writing about current trends in music and pop culture.