Outline

1. Overview of each step, covering what the step is, why it’s important, and how to implement the step at a high level.
2. Coding tutorial going through each step, how to run the code using existing tools, and alternatively how to run the same code in Mage’s workspace.
3. Conclusion
4. Additional resources

TLDR

🕵️‍♀️ Define problem

What

Clearly state the business problem you're trying to solve with machine learning and your hypothesis for how it can be solved.

Why

You need to deeply understand the problem and rationalize whether machine learning is the right approach. If you can't articulate the problem and solution, machine learning won't magically solve it.

How

1. Understand the business and user journey in depth.
2. Identify a metric to predict that impacts business goals. Determine if you have enough relevant data to predict it.
3. Decide if it will be a supervised learning problem (classification or regression) or unsupervised learning.

Tools

IDE

💾 Prepare data

What

Collect data from various sources, generate additional training data if needed, and perform feature engineering to transform the raw data into a set of useful input features.

Why

High-quality, relevant data is essential for training accurate machine learning models. Feature engineering enhances the predictive power of the input data.