What Is MLOps?

Chapter 5. Get Started with MLOps

The previous chapters have only scratched the surface on the details and nuance behind an effective MLOps system, but they do provide a good introduction to understanding why it matters and how it can affect the success of a business with data science, ML, and AI initiatives.

However, MLOps is not possible if people aren’t aligned, processes aren’t well defined, and the right technology isn’t in place to facilitate and underpin efforts. This chapter will dive into each of these areas, offering some practical lessons for getting started with MLOps in your organization.

People

As touched on in Chapter 1, the AI project life cycle must involve different types of profiles with a wide range of skills in order to be successful, and each of those people has a role to play in MLOps. But the involvement of various stakeholders isn’t about passing the project from team to team at each step—collaboration between people is critical.

For example, subject matter experts usually come to the table—or at least, they should come to the table—with clearly defined goals, business questions, and/or key performance indicators (KPIs) that they want to achieve or address. In some cases, they might be extremely well defined (e.g., “In order to hit our numbers for the quarter, we need to reduce customer churn by 10%,” or “We’re losing n dollars per quarter due to unscheduled maintenance, how can we better predict downtime?”). In other cases, less so (e.g., “Our service ...