From the course: Securing the AI/ML Development Lifecycle: A Practical Guide to Secure AI Engineering
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MLOps: Key elements and outcomes
From the course: Securing the AI/ML Development Lifecycle: A Practical Guide to Secure AI Engineering
MLOps: Key elements and outcomes
- [Instructor] Understanding the phases, roles, and key activities of MLOps is a solid first step. But to embed security successfully, we also need to understand the inputs and outputs of each. So let's walk through each of the key activities in the lifecycle with a view to what is input, what is output, and why it matters. In the prior chapter, we took a chronological view where we looked at the key activities through the lens of a triple loop: design, develop, and operate. However, in this section, let's look at the key activities functionally. This is helpful because you'll see that there are several life cycles happening in parallel, where artifacts are created, transformed, tested, measured, and ultimately released, specifically in three core areas: data, models, and code. First, let's look at data. We've discussed how data is intrinsic to the AI development process and how there are often specialized roles, such as…
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