chapter one

1 Introducing engineering principles

 

This chapter covers

  • What data scientists need to know about software engineering
  • Why data pipelines are important
  • How machine learning (ML) pipelines are used
  • Putting models into production

1.1 What do data scientists need to know about software engineering?

1.2 When do we need software engineering principles?

1.2.1 Sample data science workflow

1.2.2 How does software engineering come into the picture?

1.3 What are the components of a data pipeline?

1.3.1 Real-world example: Building a model to predict customer churn

1.4 Deploying models with machine learning pipelines

1.4.1 Data ingestion

1.4.2 Pre-processing

1.4.3 Model training

1.4.4 Model evaluation

1.4.5 Model prediction / deployment

1.4.6 Model monitoring

1.5 Summary