Summary

In this chapter, we have set the stage for understanding and implementing ML at scale using H2O.ai technology. We have defined multiple forms of scale in an enterprise setting and articulated the challenges to ML from model building, model deployment, and enterprise stakeholder perspectives. We have anchored these challenges ultimately to the end goal of ML – providing business value. Finally, we briefly introduced H2O at scale components used by enterprises to overcome these challenges and achieve business value.

In the next chapter, we'll start to understand these components in greater technical detail so that we can start writing code and doing data science.