Austin Harris

Fight Club centers on an anonymous narrator, who works as a product recall specialist for an unnamed car company. Because of the stress of his job and the jet lag brought upon by frequent business trips, he begins to suffer from recurring insomnia. When he seeks treatment, his doctor advises him to visit a support group victims to "see what real suffering is like". He finds that sharing the problems of others-despite not having testicular cancer himself-alleviates his insomnia.The narrator's unique treatment works until he meets Marla Singer, another "tourist" who visits the support group under false pretenses. The possibly disturbed Marla reminds the narrator that he is a faker who does not belong there. He begins to hate Marla for keeping him from crying, and, therefore, from sleeping. After a confrontation, the two agree to attend separate support group meetings to avoid each other. The truce is uneasy, and the narrator's insomnia returns.While on a nude beach, the narrator meets Tyler Durden, a charismatic extremist of mysterious means. After an explosion destroys the narrator's condominium, he asks to stay at Tyler's house. Tyler agrees, but asks for something in return: "I want you to hit me as hard as you can." Both men find that they enjoy the ensuing fistfight. They subsequently move in together and establish a "fight club", drawing numerous men with similar temperaments into bare-knuckle fighting matches.

Fight Club by Chuck Palahniuk Summary and Analysis

Josh Patterson

Michael Katzenellenbogen

Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform Understand the process of planning your Kubeflow deployment Install Kubeflow on an existing on-premises Kubernetes cluster Deploy Kubeflow on Google Cloud Platform step-by-step from the command line Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS Deploy and manage Kubeflow across a network of Azure cloud data centers around the world Use KFServing to develop and deploy machine learning models

Austin Harris

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