2 Your data science could use some engineering

This chapter covers

  • Elucidating the differences between a data scientist and an ML engineer
  • Focusing on simplicity in all project work to reduce risk
  • Applying Agile fundamentals to ML project work
  • Illustrating the differences and similarities between DevOps and MLOps

In the preceding chapter, we covered the components of ML engineering from the perspective of project work. Explaining what this approach to DS work entails from a project-level perspective tells only part of the story. Taking a view from a higher level, ML engineering can be thought of as a recipe involving a trinity of core concepts:

  • Technology (tools, frameworks, algorithms)
  • People (collaborative work, communication)
  • Process (software ...

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