Book Image

Automated Machine Learning Pipeline with Mesos

By : Mr. Karl Whitford
Book Image

Automated Machine Learning Pipeline with Mesos

By: Mr. Karl Whitford

Overview of this book

Mesos, with its semi-centralized infrastructure, sustains the skeleton of Silicon Valley’s Netflix (Fezo), Airbnb (Airflow), Heroku, and Apple to name a few, and has established itself as a staple in any automated machine learning pipeline and distributed heterogeneous data pruning. In this course, we will learn the foundation of Mesos within the automated pipeline on fault-tolerant cluster semaphores. We will set up a virtual cluster running Marathon and Zookeeper and a concurrent Docker application. We will establish a master-slave infrastructure, experience real-time debugging, and learn how to automate cluster arbitration via Soliton automata. We will then see an iterative queue manager for indexed tasks dispatched concurrently inside a poset topology.
Table of Contents (9 chapters)
Chapter 4
Bayesian Networks and the Mantis
Content Locked
Section 2
Resource Offers and Scheduling with Fenzo, Mesos, and Zuul
This video teaches us how does the batch approach, we barter between jobs and clusters; reducing, mapping, and extracting indexed vectorized health-checks on the block. - Understand how Zuul provides Byzantine consensus fault tolerance while maintaining resiliency between requests - Explore Fenzo usage in Mesos framework - Learn how Mantis allows different chains of jobs