Chapter 2. People of MLOps

Even though machine learning models are primarily built by data scientists, it’s a misconception that only data scientists can benefit from robust MLOps processes and systems. In fact, MLOps is an essential piece of enterprise AI strategy and affects everyone working on, or benefiting from, the machine learning model life cycle.

This chapter covers the roles each of these people plays in the machine learning life cycle, who they should ideally be connected and working together with under a top-notch MLOps program to achieve the best possible results from machine learning efforts, and what MLOps requirements they may have.

It’s important to note that this field is constantly evolving, bringing with it many new job titles that may not be listed here and presenting new challenges (or overlaps) in MLOps responsibilities.

Before we dive into the details, let’s look at the following table, which provides an overview:

Role Role in machine learning model life cycle MLOps requirements
Subject matter experts
  • Provide business questions, goals, or KPIs around which ML models should be framed.

  • Continually evaluate and ensure that model performance aligns with or resolves the initial need.

  • Easy way to understand deployed model performance in business terms.

  • Mechanism or feedback loop for flagging model results that don’t align with business expectations.

Data scientists
  • Build models that address the business question or needs brought by ...

Get Introducing MLOps now with the O’Reilly learning platform.

O’Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers.