Summary

In this chapter, we learned how to deploy ML models using web services and Docker. Although we only deployed two web services, we can see that it can become an ecosystem for ML. By separating predictions and measurements, we can separate the computational-heavy workloads (prediction) and the data collection parts of the pipeline. Since the model can be deployed on any server, we can reuse the servers and therefore reduce the energy consumption of these models.

With that, we have come to was last technical chapter of this book. In the next chapter, we’ll take a look at the newest trends in ML and peer into our crystal ball to predict, or at least guess, the future.