chapter one

1 Accelerating machine learning innovation using MLOps and Kubernetes

 

This chapter covers

  • Bringing DevOps to machine learning development using MLOps
  • Machine learning development lifecycle
  • The anatomy of an MLOps platform
  • The role of Kubernetes in MLOps

Machine learning has left the confines of academia and has transcended to become a cornerstone of modern software engineering in the past few years. In a remarkably short period, practical application of machine learning has surged, growing from identifying hot dogs and predicting the next song to play to produce intricate and innovative outcomes across a wide spectrum of domains.

While machine learning systems may appear magical to users, building such systems has proven to be difficult, even for some of the largest organizations. A 2018 Gartner study[1] found that as many as 85% of machine learning projects fail to reach production. This trend is expected to continue for years to come.

Imagine you've built an amazing machine learning model that can revolutionize your industry. You've spent countless hours fine-tuning it, and it performs exceptionally well on your test data. But then comes the real challenge – deploying it at scale in the real world. This is where many organizations stumble, and their dreams of leveraging cutting-edge AI come crashing down.

1.1 The need for speed

1.2 Developing machine learning systems

1.2.1 Establishing business goals

1.2.2 Data wrangling

1.2.3 Feature engineering

1.2.4 Model development and training

1.2.5 Model tracking

1.2.6 Inference

1.2.7 Observability

1.2.8 Rinse and repeat

1.3 MLOps platform

1.4 Benefits of building MLOps on Kubernetes

1.5 When to choose Kubernetes as your ML platform

1.6 What does this book teach?

1.7 What does this book not teach?

1.8 Summary