Automated Machine Learning Pipeline with Mesos [Integrated Course]

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Automated Machine Learning Pipeline with Mesos [Integrated Course]

Karl Whitford

Build an automated machine learning pipeline with Mesos
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Video Details

ISBN 139781788478496
Course Length2 hours 30 minutes

Video Description

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.

Key Features

  • Launch a webserver with Go and Docker
  • Gain exposure to an automated machine learning pipeline
  • Validate your learning with assessments and quizzes

Who this course is for

This course is targeted at data science professionals looking to get started with Mesos to integrated it in their machine learning pipeline.

Table of Contents

Learning from Trees (Benchmarking)
The Course Overview
Challenges
Mesos with Vagrant Setup with Centos-7
Actor Programming Model
CNI with Weave and Docker
Automatically Deploy Multiple Clusters
Mesos Containerized Deployment with Calico
Vagrant Mesos Multi-Node Cluster Dispatch
Policy Driven CNI with Semaphores with Calico
Bayesian Networks and the Mantis
The Mantis Under the Hood(Hive Search)
Resource Offers and Scheduling with Fenzo, Mesos, and Zuul
The Post PageRank Era: Apache Pregel, Quegel, and Frego
Vertex State Machines in Bulk Synchrony
Superstep Pregel Model of DAG Inference
Go Protocol Buffers and Token Partitioners with Cassandra
Build the FreGo Job Graph with Golang
Replicated Logs and Greedy Frameworks
Sparsity Awareness
Markovian Random Fields and Convolutional Nets with TensorFlow
Concatenating Jenkins Utilities
Cassandra Heartbeat Part.1
Filter Automata
Beyond the Servlet: Webpack Bundles
Webpack Bundles, Syntactic Parser, and Task Dispatcher
Markov Queues and Stochastic Context Free Grammar
DC/OS Review
Cost-Based Resource Allocator
Distributed MapReduce for Streaming Workflows
Collecting Learning Data from Network Metrics
Do You Model Search?

What You Will Learn

  • Set up Mesos with Vagrant in CentOS 7
  • Understand job scheduling and reconciliation
  • Learn about multi-tenancy
  • Build real-time dashboards with Node,js and React.js using Webpack
  • Understand concurrent data pruning
  • Extract components using React.js

Authors

Table of Contents

Learning from Trees (Benchmarking)
The Course Overview
Challenges
Mesos with Vagrant Setup with Centos-7
Actor Programming Model
CNI with Weave and Docker
Automatically Deploy Multiple Clusters
Mesos Containerized Deployment with Calico
Vagrant Mesos Multi-Node Cluster Dispatch
Policy Driven CNI with Semaphores with Calico
Bayesian Networks and the Mantis
The Mantis Under the Hood(Hive Search)
Resource Offers and Scheduling with Fenzo, Mesos, and Zuul
The Post PageRank Era: Apache Pregel, Quegel, and Frego
Vertex State Machines in Bulk Synchrony
Superstep Pregel Model of DAG Inference
Go Protocol Buffers and Token Partitioners with Cassandra
Build the FreGo Job Graph with Golang
Replicated Logs and Greedy Frameworks
Sparsity Awareness
Markovian Random Fields and Convolutional Nets with TensorFlow
Concatenating Jenkins Utilities
Cassandra Heartbeat Part.1
Filter Automata
Beyond the Servlet: Webpack Bundles
Webpack Bundles, Syntactic Parser, and Task Dispatcher
Markov Queues and Stochastic Context Free Grammar
DC/OS Review
Cost-Based Resource Allocator
Distributed MapReduce for Streaming Workflows
Collecting Learning Data from Network Metrics
Do You Model Search?

Video Details

ISBN 139781788478496
Course Length2 hours 30 minutes
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