1 Introduction to serverless machine learning


This chapter covers

  • What serverless machine learning is and why you should care
  • The difference between machine learning code and a machine learning platform
  • How this book teaches about serverless machine learning
  • The target audience for this book
  • What you can learn from this book

A Grand Canyon—like gulf separates experimental machine learning code and production machine learning systems. The scenic view across the “canyon” is magical: when a machine learning system is running successfully in production it can seem prescient. The first time I started typing a query into a machine learning—powered autocomplete search bar and saw the system anticipate my words, I was hooked. I must have tried dozens of different queries to see how well the system worked. So, what does it take to trek across the “canyon?”

1.1 What is a machine learning platform?

1.2 Challenges when designing a machine learning platform

1.3 Public clouds for machine learning platforms

1.4 What is serverless machine learning?

1.5 Why serverless machine learning?

1.5.1 Serverless vs. IaaS and PaaS

1.5.2 Serverless machine learning life cycle

1.6 Who is this book for?

1.6.1 What you can get out of this book

1.7 How does this book teach?

1.8 When is this book not for you?

1.9 Conclusions