Four Key Stages of The Machine Learning Process

These four stages is defined by Emmanuel Ameisen in his recent book Building Machine Learning Powered Applications. As I think it is an important piece, I created a process diagram above and note down few important points for each stage. You can considered this is a study note.

1. Identifying the Right ML Approach

The best approach for a given problem (product goal) will depends on success criteria, data availability, task complexity and model choice.

2. Building an Initial Prototype

Start by building an end-to-end prototype before working on a model which aim to tackle the product goal with no ML involved and will allow you to determine how to best apply ML.

3. Iterating on Models

If you need ML, start gathering dataset, train a model and evaluate it shortcomings. The goal of this stage is to repeatedly alternate between error analysis and implementation.

4. Deployment & Monitoring

Once a model shows good performance, you should pick an adequate deployment option. Once deployed, models often fail in unexpected ways. The last two chapters of the book will cover methods to mitigate and monitor model errors.

Side Note

I added an arrow labelled "New product goal, Revised product goal" to the process diagram. Based on my experience, software products evolve, it will not end there after the ML solution went live. Hence, it is likely that the existing product goal will be revised or new product goal being inspired or discovered. Then, the cycle start from stage 1. Don't you think so?