Introduction

Machine learning is in many ways the intersection of two disciplines - data science and software engineering. The goal of machine learning is to use data to create a predictive model that can be incorporated into a software application or service. To achieve this goal requires collaboration between data scientists who explore and prepare the data before using it to train a machine learning model, and software developers who integrate the models into applications where they're used to predict new data values (a process known as inferencing).

Machine learning has its origins in statistics and mathematical modeling of data. The fundamental idea of machine learning is to use data from past observations to predict unknown outcomes or values. For example:

• The proprietor of an ice cream store might use an app that combines historical sales and weather records to predict how many ice creams they're likely to sell on a given day, based on the weather forecast.
• A doctor might use clinical data from past patients to run automated tests that predict whether a new patient is at risk from diabetes based on factors like weight, blood glucose level, and other measurements.
• A researcher in the Antarctic might use past observations to automate the identification of different penguin species (such as Adelie, Gentoo, or Chinstrap) based on measurements of a bird's flippers, bill, and other physical attributes.