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)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Networking on Kubernetes

Kubernetes operates a flat private network among all the resources in a Kubernetes cluster. Within a cluster, all Pods can communicate with each other cluster-wide without an network address translation (NAT). Kubernetes gives each Pod its own cluster private IP address, and the IP is the same IP seen by the Pod itself and what others see it as. All containers inside a single Pod can reach each container's port on the localhost. All nodes in a cluster have their individually assigned IPs as well and can communicate with all Pods without an NAT. The following figure (Figure 6.6) shows the different IP assignments for Pods and nodes, and communication flows from different resources:

Figure 6.6 – IP assignments and communication flow

Sometimes you might need a set of Pods running the same application container (the container for an Nginx application) for high availability and load balancing, for example. Instead of calling...