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Alfresco 4 Enterprise Content Management Implementation

Video Description

Build an automated machine learning pipeline with Mesos

About This Video

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

In Detail

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.

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Learning from Trees (Benchmarking)
    1. The Course Overview 00:02:34
    2. Challenges 00:08:02
    3. Mesos with Vagrant Setup with Centos-7 00:10:06
  2. Chapter 2 : Actor Programming Model
    1. CNI with Weave and Docker 00:08:29
    2. Automatically Deploy Multiple Clusters 00:03:56
  3. Chapter 3 : Mesos Containerized Deployment with Calico
    1. Vagrant Mesos Multi-Node Cluster Dispatch 00:04:12
    2. Policy Driven CNI with Semaphores with Calico 00:08:24
  4. Chapter 4 : Bayesian Networks and the Mantis
    1. The Mantis Under the Hood (Hive Search) 00:06:03
    2. Resource Offers and Scheduling with Fenzo, Mesos, and Zuul 00:05:37
    3. The Post PageRank Era: Apache Pregel, Quegel, and Frego 00:10:40
  5. Chapter 5 : Vertex State Machines in Bulk Synchrony
    1. Superstep Pregel Model of DAG Inference 00:05:53
    2. Go Protocol Buffers and Token Partitioners with Cassandra 00:07:07
    3. Build the FreGo Job Graph with Golang 00:08:01
  6. Chapter 6 : Replicated Logs and Greedy Frameworks
    1. Sparsity Awareness 00:07:24
    2. Markovian Random Fields and Convolutional Nets with TensorFlow 00:06:55
  7. Chapter 7 : Concatenating Jenkins Utilities
    1. Cassandra Heartbeat Part.1 00:07:17
    2. Filter Automata 00:04:03
  8. Chapter 8 : Beyond the Servlet: Webpack Bundles
    1. Webpack Bundles, Syntactic Parser, and Task Dispatcher 00:08:35
    2. Markov Queues and Stochastic Context Free Grammar 00:03:23
    3. DC/OS Review 00:07:35
  9. Chapter 9 : Cost-Based Resource Allocator
    1. Distributed MapReduce for Streaming Workflows 00:05:32
    2. Collecting Learning Data from Network Metrics 00:08:50
    3. Do You Model Search? 00:09:28