Kubernetes for MLOps - Scaling Enterprise Machine Learning, Deep Learning, and AI

✍ Scribed by Sam Charrington

Tongue: English
Leaves: 31
Series: This Week in ML
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Enterprise interest in machine learning and artificial intelligence continues to grow, with
organizations dedicating increasingly large teams and resources to ML/AI projects. As
businesses scale their investments, it becomes critical to build repeatable, efficient, and
sustainable processes for model development and deployment.
The move to drive more consistent and efficient processes in machine learning parallels
efforts towards the same goals in software development. Whereas the latter has come to be
called DevOps, the former is increasingly referred to as MLOps.
While DevOps, and likewise MLOps, are principally about practices rather than technology, to
the extent that those practices are focused on automation and repeatability, tools have been
an important contributor to their rise. In particular, the advent of container technologies like
Docker was a significant enabler of DevOps, allowing users to drive increased agility, efficiency,
manageability, and scalability in their software development efforts.
Containers remain a foundational technology for both DevOps and MLOps. Containers provide
a core piece of functionality that allow us to run a given piece of code—whether a notebook,
an experiment, or a deployed model—anywhere, without the “dependency hell” that plagues
other methods of sharing software. But, additional technology is required to scale containers
to support large teams, workloads, or applications. This technology is known as a container
orchestration system, the most popular of which is Kubernetes.

✦ Table of Contents

Table of Contents
Preface to the Second Edition...............................................................4
Introduction...........................................................................................5
The Machine Learning Process.............................................................6
Machine Learning at Scale..................................................................10
Enter Containers and Kubernetes........................................................14
Getting to Know Kubernetes................................................................15
Kubernetes for Machine and Deep Learning........................................18
MLOps on Kubernetes with Kubeflow..................................................20
The Kubernetes MLOps Ecosystem.....................................................23
Case Study: Volvo Cars........................................................................23
Getting Started....................................................................................25
About TWIML.......................................................................................29

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