14 Training and deployment pipeline

This chapter covers

  • Feeding models training data in a production environment
  • Scheduling for continuous retraining
  • Using version control and evaluating models before and after deployment
  • Deploying models for large-scale on-demand and batch requests, in both monolithic and distributed deployments

In the previous chapter, we went through the data pipeline portion of an end-to-end production ML pipeline. Here, in the final chapter of the book, we will cover the final portion of the end-to-end pipeline: training, deployment, and serving.

To remind you with a visual, figure 14.1 shows the whole pipeline, borrowed from chapter 13. I’ve circled the part of the system we’ll address in this chapter.

Figure 14.1 ...

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