Reviewing model development

Model development includes discovering relationships between data and features and better understanding the context of the business question being solved. This may also be a good time to understand KPIs and success measures, as well as the overall structure of the business problem. Performing descriptive statistical analysis and creating data visualizations are also ideal activities at this stage of the pipeline.

As you learned in previous chapters, you can perform data analysis and model development in Python, as well as R. Python offers a number of useful packages that we’ve already discussed, including Keras, TensorFlow, and PyTorch. There are also “auto-ML” frameworks where models can be developed and run in the cloud, including Google AutoML, Azure ML Studio, Amazon SageMaker, IBM Watson, Databricks AutoML, H20, and Hugging Face.

We will skip over the details of ML development, since we already discussed them at length in...