2020 Cloud Computing and Big Data Lecture 5 Map-Reduce Computing Paradigm Part2 ?

2020 Cloud Computing and Big Data Lecture 5 Map-Reduce Computing Paradigm - Part Two ? Parallel & Scalable Machine Learning & Deep Learning 16 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Fall 2020 Course Outline 1. Cloud Computing & Big Data Introduction 2. Machine Learning Models in Clouds 3. Apache Spark for Cloud Applications 4. Virtualization & Data Center Design 5. Map-Reduce Computing Paradigm 6. Deep Learning driven by Big Data 7. Deep Learning Applications in Clouds 8. Infrastructure-As-A-Service (IAAS) 9. Platform-As-A-Service (PAAS) 10. Software-As-A-Service (SAAS) 11. Big Data Analytics & Cloud Data Mining 12. Docker & Container Management 13. OpenStack Cloud Operating System 14. Online Social Networking & Graph Databases 15. Big Data Streaming Tools & Applications 16. Epilogue Lecture Outline Part One: Map Reduce Approach Big Data Analytics Requirements & Map-Reduce via Apache Hadoop Motivation for Hiding Computing Complexity & Increase Reliability Divide and Conquer Paradigm & Three ‘phases’ of Map-Reduce Understanding Key-Value Data Structures for Map-Reduce Open Source Apache Hadoop & HBase in Facebook Applications Part Two: Map-Reduce Ecosystem in Clouds & Applications Role of Reliability using Statistics & Hadoop Distributed File System Replication Concept & Benefits for Cloud ‘Big Data’ Applications Amazon Web Services offering Elastic MapReduce & Ecosystem Tools Using Containers with Docker & Cloud Deployment using Kubernetes OpenStack Cloud Operating System & Map-Reduce Limits

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