Unsupervised Clustering in Mesos

Video description

This is a hands-on course which helps learn soliton cluster isolation system for unsupervised clustering in Mesos

About This Video

  • In-depth coverage of Inference matroids
  • Perform granular synthesis with druid streams
  • Write a custom isolator module for Mesos
  • Perform MCMC anomaly detection
  • Understand the actor dining model and docker port mappings

In Detail

Apache Mesos is an open source cluster manager that handles workloads in a distributed environment through dynamic resource sharing and isolation. Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines (physical or virtual), enabling fault-tolerant and elastic distributed systems to easily be built and run effectively.

This course begins with an introduction to Inference matroids wherein you will learn about vertex combiners with Hama, Graph Isomorphism, Soliton, and DAGs. Then you will learn to perform granular synthesis with druid streams and to write custom isolator module for Mesos. Next, you will be introduced to RoBo and will learn to manifold the cluster trees . Then you will understand what Pythonic Clojars and Monads are. Further, you will become familiar with the actor dining model and port mappings. Finally, you will learn to auto-scale clusters.


This course is for journeyman distributed data center enthusiasts and Mesos professionals in the industry with a strong foundation in Stochastic Calculus, Statistical Learning, Pattern Recognition, Algorithms and Data Structures with Graphs, Queues, Heaps, Stacks, and more. Also, you should have proficient working knowledge of search algorithms, linear optimization, dynamic programming. Having knowledge of Software Engineering principles such as Finite State Machines, Priority Queues, Linked Lists, Adjacency Lists, Hash Tables, BFS, DFS, and Cellular Automats will be beneficial.

Publisher resources

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Product information

  • Title: Unsupervised Clustering in Mesos
  • Author(s): Karl Whitford
  • Release date: January 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781788479677