Machine Learning Life Cycle

Last updated on May 20th, 2025 at 04:24 am

Machine Learning is the backbone of every AI-based system. It allows these systems to process data, learn information from it and apply the knowledge to solve problems. Machine Learning consists of various stages. Each stage is important to ensure that you build a robust solution. So it is important to understand how machine learning works. In this article, we will learn about the different stages of machine learning life cycle.

Machine Learning Life Cycle

Let us learn about the different stages of machine learning life cycle. Here is a snapshot of all the stages of machine learning.

1. Problem Definition

Unless we know why we need a machine learning system, we cannot build it properly. So the first step is to clearly define the problem statement that is to be solved by your machine learning system. It is good to be as specific as possible. For example, ‘increase sales by 10% in next 6 months’ is better than ‘grow sales this year’.

Basically, your problems statement must be measurable and achievable. It should be created after consulting the appropriate stakeholders, both business and technical. You should also lay out desired outcomes as well as project scope.

The problem statement determines the entire direction of your project. If it is not picked correctly, then you may build a machine learning system that does not serve your purpose.

2. Data Gathering

Once you have defined the problem statement, you will need to collect data that can be used to train your model. The quality of your data will determine the quality of knowledge derived from it, and consequently the performance of your machine learning system. Here are some key points to remember while collecting data.

1. The collected data must be relevant to your problem statement, otherwise your model will not be able to help you much.
2. Data should be of good quality. It should not contain errors and should be legally sourced without stealing/piracy/privacy concerns. Also, it should be credible.
3. The volume of data should be sufficiently large so as to train your model properly.
4. The data should have enough diversity to train your model about different problem scenarios.

You need to pay special attention to your data, because what goes in, comes out. If your data is erroneous and full of issues, then your machine learning model will be trained incorrectly. Its knowledge will be faulty and it will keep giving wrong insights and solutions to your problems.

3. Data Preparation

In most cases, the data that you source is often unstructured and raw. If you use multiple data sources, then they may be in different formats altogether. They may also contain errors, duplicates and inconsistencies. Such data cannot be directly fed into your models since it will be incompatible. So it is important to clean up your data before you use it.

You will need to first standardize the data format and combine multiple data sources together. You will also need to standardize values across datasets so that they mean the same thing in all data sources. If your data is stored in or can be imported to Pandas dataframes, then you can merge dataframes to combine datasets. Then you will need to remove erroneous values, duplicates, missing values and outliers, if any.

Lastly, ensure that your data is well organized and in the appropriate format that can be used by your machine learning system.

4. Data Wrangling

In the above step, you have not created any new data. We have simply cleaned up the existing data, and standardized its format. Sometimes, you may also need to carry out some transformations in your data to create new data sets that can be fed in your system. For this purpose, you may need to use certain ETL reporting tools.

5. Data Analysis

In this stage, we basically try to understand our data after preparing it. This is required so that you understand what you are feeding into your machine learning system, and what you can expect from it. This involves data visualization & reporting, data summarization and statistical analysis. You can also use create pivot tables to quickly summarize data.

This is useful in quickly spotting outliers, patterns and trends. This is also useful in feature engineering and model selection, where you decide what kind of model to employ for your system and what features to include in your machine learning system.

6. Model Development

In this stage, we need to do two things – model selection and model training.

Model selection involves determining the different parts of your model such as training algorithm. Ensure that it is relevant to your problem statement as well as the data you have. Sometimes, you may find a model that is suitable for problem statement but incompatible with data. Sometimes, it is the other way around. So it is important to try out different models to see which one fits your requirements better.

Next, you need to train the model using your data. There are several ways to train your model – supervised, unsupervised, semi-supervised and reinforced learning. Depending on your data and training method, you need to train your model over multiple iterations.

7. Model Testing

Once you have fed the data into your model and obtained output, it is essential to test it. There are several performance metrics to do this. You can check the accuracy of your model that tells you how often your model gives the right answer to problems. You may check its precision too, which tells you how many predictions are correct. In addition to these, you can also use recall and f1 score. Recall tells you the exact positive outcomes your model found. F1 score is a combination of precision and recall.

This is an iterative process and may require multiple attempts to fine tune the results. You may need to change some of the parameters for certain outliers.

8. Deployment

Lastly, we need to deploy our machine learning system into real-world scenario so that it can analyze real-world data and provide solutions. While doing so, you need to carefully integrate it with existing systems so that they are not disrupted and there is a smooth exchange of data between systems.

You can also create an API for your machine learning system that allows other systems to easily communicate with it.

You need to also keep an eye on scalability of your system. Otherwise, it may crash as its usage increases over time.

Based on your model’s performance, you must also regularly fine tune your system to improve its performance.

Conclusion

In this article, we have learnt about machine learning life cycle. We learnt about the different stages involved in machine learning and what to do in each stage. Every stage is important and needs to be planned and executed carefully. It is also important to understand that each stage itself is iterative, requires regular monitoring and optimization. There are many popular Python libraries for machine learning that you can use to avoid working from scratch. Building a robust machine learning system will help you get desired results, solve problems and seamlessly integrate with other systems.

Also read:

How Machine Learning Works
How to Copy Data from One Dict to Another
How to Convert String to List in Python