Category: End to End ML Project

End-to-End Machine learning project Tutorial

Being able to lead an End-to-End Machine learning project is such a crucial skill that many times it is hard to learn by yourself due to the complexity of each step along the way. Our goal in the next tutorial and series of posts is to demystify the complexity by unpacking each step in the Stack. By the end of this guide, you will feel equipped with many new skills and hands-on experience!

Walk with me on an amazing journey of creating an End-to-End Machine learning project. From Data preparation, and model training, to deployment.

What you will learn you ask?

1. Introduction to End-to-End ML Projects

   1. Overview of the end-to-end machine learning project process.
   2. Discuss the importance of each step and its role in the project.
   3. Present the problem to be solved in the upcoming tutorial (choose a practical problem to solve).

2. Problem Understanding and Definition

   1. Deep dive into the selected problem.
   2. Discuss how to define the problem in ML terms (classification, regression, etc.).
   3. Discuss the metrics to evaluate the model.

3. Data Collection and Preparation

   1. Discuss different methods to collect data.
   2. Talk about data cleaning and preparation.
   3. Acquire our dataset for the rest of the project.
   4. Demonstrate how to perform exploratory data analysis.

4. Feature Engineering

   1. Explain what feature engineering is and its importance.
   2. Show different techniques of feature engineering.
   3. Apply these techniques to the collected data.

5. Model Selection and Training

   1. Discuss various ML models that could be used for the problem.
   2. Show how to train these models on the prepared dataset.
   3. Discuss overfitting and how to avoid it.

6. Hyperparameter Tuning

   1. Discuss the concept of hyperparameters in ML models.
   2. Explain different techniques for hyperparameter tuning.
   3. Apply these techniques to the models trained in Post 5.

7. Model Evaluation and Selection

   1. Discuss different evaluation metrics that could be used for the problem.
   2. Evaluate the models trained in Post 5 using these metrics.
   3. Select the best model based on the evaluations.

8. Model Deployment

   1. Discuss different ways to deploy ML models (e.g., cloud, on-premises, edge devices).
   2. Discuss monitoring and maintaining the model once it is deployed.
   3. Complete our End to End ML

      End-to-End Machine learning project series

      Project by deploying the selected model from Post 7.

9. Iteration and Improvement

   1. Discuss how to iterate on the ML project to improve its performance.
   2. Cover refining the model, feature engineering, and other ways to improve the model.
   3. How do we know we should re-train our model?

And many more…

No better way to describe this tutorial other than – “End-to-End Machine learning project”