MACHINE LEARNING AND CLOUD COMPUTING

Cloud Computing and Machine Learning are becoming synergic in the sense that data analytics and machine learning models at scale is possible with cloud with its ability to store unlimited amounts of data and processing power to do analytics and machine learning on Big data.

Course Content :

Module-1

  • Introduction to Cloud Computing
  • Cloud as Utility
    • Software as a service (SaaS)
    • Platform as a service (PaaS)
    • Infrastructure as a service (IaaS)
  • Cloud Systems – Public Cloud, Private Cloud, Hybrid Cloud
  • Virtualization, Container
  • Assignment 1 – Auto Scaling in Cloud – Working with AWS – Working with GCP – Working with Azure

Module-2

  • Introduction to Big Data and Hadoop
  • Distributed Computing
  • Hadoop Architecture
  • Hadoop Distributed File Systems
  • MapReduce Programming Models
  • Assignment 2 – MapReduce Programming

Module-3 

  • Introduction to Spark
  • Spark Architecture
  • Application Execution Flow in Spark
  • Spark Memory Management
  • Sparker-Optimizing Spark for Heterogeneous Clusters
  • Heterogeneity in Cloud Platforms
  • Executors in Spark
  • Tula
  • Limitations of Spark’s resource provisioning in Heterogeneous Clusters
  • GAS ( GPU Assisted Process Scheduling for Multicore systems )
  • Assignment 3 – Setup spark cluster, Running ALS and FP growth Tree algorithm.

 

Module-4

  • Basics of Machine Learning
  • Graph Processing – Tensorflow
  • Assignment 4 – Tensorflow

Module-5

  • Deployment of private cloud and integrating with public cloud
  • Assignment 5 – Scaling from private to public cloud

Module-6

  • Nosql Database Systems

Final Project – Project selection should be from recommended publications based on your interest.

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