Developing and Deploying ML Models
In the previous chapter, we discussed the preparation stage for the ML process, including problem framing and data preparation. After we have framed the problem and have a clean dataset, it’s time to develop and deploy the ML model. In this chapter, we will discuss the model development process. We will start from model data input and hardware/software platform setup, then focus on the model development pipeline, including model training, validation, testing, and finally deploying to production. Our emphasis is on understanding the basic concepts and the thought processes behind them and strengthening the knowledge and skills by practicing. The following topics are covered in this chapter:
- Splitting the dataset
- Building the platform
- Training the model
- Validating the model
- Tuning the model
- Testing and deploying the model
- Practicing with scikit-learn
In Appendix 3, we provide practice examples of ML model...
