Chapter 1: Opportunities and Challenges

Machine Learning (ML) and data science are winning a popularity contest of sorts, as witnessed by their headline coverage in the popular and professional press and by expanding job openings across the technology landscape. Students typically learn ML techniques using their own computers on relatively small datasets. Those who enter the field often find themselves in the much different setting of a large company buzzing with workers performing specialized job roles, while collaborating with others scattered across the nation or world. Both data science students and data science workers have a few key things in common – they are in an exciting and growing field that businesses deem ever more critical to their future, and the data they thrive on is becoming exponentially more abundant and diverse.

There are huge opportunities for ML in enterprises because the transformational impacts of ML on businesses, customers, patients, and so on are diverse, widespread, lucrative, and life-changing. A backdrop of urgency exists as well from competitors who are all attempting the same thing. Enterprises are thus incented to invest in significant ML transformations and to supply the necessary data, tooling, production systems, and people to journey toward ML success. But challenges loom large as well, and these challenges commonly revolve around scale. The challenges of scale take on many forms inherent to ML at an enterprise level.

In this chapter, we will define and explore the challenge of ML at scale by covering the following main topics:

• ML at scale
• The ML life cycle and three challenge areas for ML at scale
• H2O.ai's answer to these challenges