chapter one

1 What is a Machine Learning Engineer?

 

This chapter covers

  • Defining Machine Learning Engineering and why it is important to increase the chances of successful ML project work
  • Explaining why ML Engineering and the processes, tools, and paradigms surrounding it can minimize the chances of ML project abandonment
  • Discussing the six primary tenets of ML Engineering and how failing to follow them causes project failure

Machine learning (ML) is exciting. To the layperson, it brings with it the promise of seemingly magical abilities of soothsaying; uncovering mysterious and miraculous answers to difficult problems. ML makes money for companies, it autonomously tackles overwhelmingly large tasks, and relieves people from the burden of monotonous work involved in analyzing data to draw conclusions from. To state the obvious, though, it’s challenging. From thousands of algorithms, a diverse skill set ranging from Data Engineering (DE) skills to advanced statistical analysis and visualization, the work required of a professional practitioner of ML is truly intimidating and filled with complexity.

1.1      Why ML Engineering?

1.2      The core components of ML Engineering

1.2.1   Planning

1.2.2   Scoping & Research

1.2.3   Experimentation

1.2.4   Development

1.2.5   Deployment

1.2.6   Evaluation

1.3      The goals of ML Engineering

1.4      Summary