Enterprise AI in the Cloud

18Deploying Your Models Into Production

Do not wait to strike till the iron is hot but make it hot by striking.

—William Butler Yeats

Having navigated through data processing, algorithm selection, model training and fine-tuning, you are now on the verge of an important step: model deployment. This is an essential chapter for data scientists, ML engineers, IT professionals, and organizational leaders involved in deploying models into production using the cloud.

The true value of an AI model is not just in its design or accuracy but in its real-world use. This chapter dives deep into the nuances of deploying your model, from understanding the challenges in model deployment, monitoring, and governance to deciding between real-time and batch inferences.

Model deployment isn't just about pushing a model live. It involves strategic decisions, systematic processes, and synchronized architecture. Keep in mind that you will not just be launching models into production; you will also be ensuring that the models are functioning optimally and responsibly within your larger AI ecosystem. See Figure 18.1.

The focus of this chapter, though, is to have a successfully deployed model that is making the impact that it was meant to have during the design process to achieve continuous innovation and growth.

STANDARDIZING MODEL DEPLOYMENT, MONITORING, AND GOVERNANCE

Here are some considerations for your model deployment, monitoring, and governance process:

• Automate deployment: You should automate ...