Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
Table of Contents (17 chapters)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Kubernetes overview and core concepts

While it is feasible to deploy and manage the life cycle of a small number of containers and containerized applications directly in a compute environment, it can get very challenging when you have a large number of containers to manage and orchestrate across a large number of servers. This is where Kubernetes comes in. Initially released in 2014, Kubernetes (K8s) is an open source system for managing containers at scale on clusters of servers (the abbreviation K8s is derived by replacing ubernete with the digit 8).

Architecturally, Kubernetes operates a master node and one or more worker nodes in a cluster of servers. The master node, also known as the control plane, is responsible 
for the overall management of the cluster, and it has four key components:

  • API server
  • Scheduler
  • Controller
  • etcd

The master node exposes an API server layer that allows programmatic control of the cluster. An example of an API...