14Operating Your AI Platform with MLOps Best Practices
The value of an idea lies in the using of it.
—Thomas Edison
Now that the AI platform has been built, your focus shifts toward operating the AI platform; ensuring the efficiency, reliability, and repeatability of the ML operations workflow; and enabling scale with automation (see Figure 14.1).
The target audience includes tech leads, operations managers, ML engineers, data scientists, systems administrators, and IT professionals looking to employ automation in daily operations.
Automation using MLOps isn't just a buzzword. It's the secret sauce to scaling rapidly, reducing manual errors, speeding up processes, and enabling adaptability in a dynamic business landscape.
In this chapter, you review various key components, from model operationalization and deployment scenarios to automation pipelines, platform monitoring, performance optimization, security, and much more. It includes actionable deliverables that will help you crystallize the MLOps ideas presented in this chapter.
This chapter serves as a pivotal transition point from setting up your AI infrastructure (Chapter 13) to actual data processing and modeling (Chapters 15–17), highlighting the role of MLOps in ensuring this transition is smooth, efficient, and automated.
CENTRAL ROLE OF MLOps IN BRIDGING INFRASTRUCTURE, DATA, AND MODELS
This section discusses the role that the MLOps best practices in this chapter play in ensuring automation and integration across ...
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