A Machine Learning Project Life Cycle.
A Detailed Step-by-Step Process of Machine Learning Project.
Let’s Dive into The World of Machine Learning!!
Machine Learning has become the most demanding and powerful tool in different domains of several industries in this digital era to solve many complex problems by revolutionizing the way of approaching those problems.
From Predicting the behavior of a customer to automating many tasks, Machine learning has shown its capacity to convert raw data into actionable insights.
Even though converting raw data into actionable insights is not determined by ML algorithms alone. The success of any ML project depends on a well-structured lifecycle.
In this article, I am going to explain in detail step-by-step approaches or stages of the machine learning project lifecycle.
Define the Scope of the project
Collect & Explore Data
Data organization and Feature Engineering
Model Preparation & Model Training
Model Evaluation by Error & measured Analysis
Model Deployment in Production
Monitor & Maintain ML Model
Take the response from the Model & Continuous Improvement
Step I: Define the Scope of the project
- It is important to have the scope in hand to solve any problem. As humans, we need to fix the scope based on the problem we want to solve using machine learning.
- Therefore, we will collaborate with domain experts to define the project objectives and success of a project.
- It is important to have clarity about the scope of your project, which can be gained by doing a lot of research and asking as many questions as possible to find the impact of solving that particular problem using Machine learning.
- This is the first and crucial stage to define and set the foundation for the entire project, and we need to ensure that our solutions will solve the problem of customers' goal fulfillment.
The scope of machine learning has a wide range of scopes, and each of those solves specific problems. Let’s look at a few of them, for example.
- Regression Project: finding house price estimation, stock price, …. etc.
- Classification Project: email spam detection, titanic survival prediction,…. etc.
- NLP Project: Speech recognition, chatbots, ….. etc.
- Recommendation Project: Movie recommendation, video recommendation, ……. etc.
There are many other types of problems we will be solving. Therefore, we need to fix the scope of the project at the start, and we also need to have an idea about what metrics we need to find out to ensure that the problem is solved efficiently.
Step II: Collect & Explore Data
- After defining the scope, we need data on which we will work. Once you collect the data from any source, we need to ensure that the data is qualitative.
- If not it is our responsibility to make it qualitative and relevant to solve problems efficiently.
- As a data scientist, we will explore the entire data set to understand each characteristic and identify any patterns existing if any in it. This process is called Exploratory Data Analysis(EDA).
Step III: Data organization and Feature Engineering
- This is a crucial step to get accurate results. This process involves cleaning and transforming the data into our required formats that are appropriate to ML model training.
- Also, we need to handle any missing values present if any, and make sure that we should normalize the numerical data or encode the categorical data.
- Feature engineering is another important process that involves creating new features or changing existing features to improve the model's performance.
Step IV: Model Preparation & Model Training
- It is important to choose the right machine learning model or algorithm to solve any specific problem; it is a wise decision.
- We can’t ensure that the model is accurate but we can predict which model will give us the right results based on the problem and the expected result with a given data set.
- As I mentioned few models in the first step as in the scope of the project like Regression, NLP, classification, ……. etc.
- Whatever the selected model, we will train the model with the fixed data as training data, and that will predict the results.
- Therefore, the model will give us results, but we expect the results to match when we apply the model, so it is okay. Still, If not need to make some changes in parameters and then iterate the entire process of training the model which is called hyperparameter tuning, With this process we can achieve our required results.
- Parameter tuning will work as optimization for any machine learning model to predict the results more accurately.
Step V: Model Evaluation by Error & measured Analysis
- Once we prepare the model, the model’s performance can be done by using different data sets, i.e., validation data.
- There are many metrics we generally use to evaluate the performance of the model, all those metrics depend on the problem or scope of the project.
- For example, a few metrics we will find are Accuracy, Precision, MAE, R-squared, MSE, F1 score, recall, Region of convergence, ……. etc.
- This step helps us predict our prepared model’s performance, and we can identify major issues like whether our model is overfitting the data set or underfitting can easily find out.
- Depending upon these metrics and issues, we will again start iterating the model preparation or continue to deploy it into production can be decided.
Step VI: Model Deployment in Production
- If we get a good enough model from the previous step, then in this step, we deploy it into a production environment.
- This is a very crucial stage where our model will be integrated with real system data, which we generally call testing data.
- As a data scientist, we will the responsible for ensuring that our model is more scalable, reliable, and compatible within the production environment.
Step VII: Monitor & Maintain ML Model
- After we deploy the model into the production environment, it is important to monitor how our model performing and giving results as we expected or not since machine learning models are not at all static.
- As a data scientist, we should keep an on eye how our model works, if needed we need to do some maintenance in such a way that our model is more effective in giving results.
- Therefore, this process will give us the model’s performance in a production environment by detecting any float in the input data distribution and retraining our model if it is required.
Step VIII: Take the response from the Model & Continuous Improvement
- Data Scientists are regularly updating and making improvements in the model about changes in input data to deliver accurate results.
- Therefore, this entire process is not a single process, it keeps on reflecting our input into output and vice versa by model improvements as it works as a looping system.
- The model response is crucial to finding future iterations of the project and any changes in model preparations that can be made to achieve satisfactory results.
Let’s Conclude
Finally, we can say the machine learning project life cycle is a dynamic and iterative process that needs rights planning, collaboration, and continuous improvements.
Every stage in this life cycle plays a crucial role in predicting accurate results. By ensuring the best practices in each step, data scientists and machine learning engineers can increase the accuracy of predictions to increase impactful solutions.
I hope this article helps you with a basic understanding of how we can develop and deploy machine learning projects in real-world projects.
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Reference: DeepLearning.AI by Andrew Ng.
