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Course Detail

Course Name Cloud and Big Data Analytics
Course Code 24AI750
Program M. Tech. in Artificial Intelligence
Credits 3
Campus Amritapuri ,Coimbatore

Syllabus

Cloud computing fundamentals – Principles of Cloud Computing Systems, Elastic Cloud Systems for Scalable Computing, Cloud Architectures Compared with Distributed Systems, Service Models, Ecosystems and Scalability Analysis, Building Compute Service – Storage Service – Databases Service – Serverless Models on Cloud.

 

Frameworks for Big data: Hadoop – Hadoop Framework – Hadoop Daemon – Map Reduce Programming- Hadoop Ecosystem – Spark – Framework – RDD – Advanced RDD – Structured data – SQL, Dataframes, and Datasets – Streaming in Spark – Spark Distributed Processing – Building Spark ML on Cloud platform.

 

Cloud dataflow – dataflow templates, data transformation with cloud dataflow, working with apache beam, cloud publisher subscriber – architecture, message flow, implementation. Cloud data processing. Introduction to Containers and Kubernetes in Google Cloud, Introduction to AI platform pipelines

 

Objectives and Outcomes

Preamble

There is an unprecedented amount of data that is generated in today’s world by both humans and machines. Being able to store, manage, analyze, and building intelligent applications has a critical impact on business, scientific discovery, social and environmental challenges. This course helps students to use cloud platform with its tools and distributed computing techniques to quickly build prototypes and applications for scalable data and workloads.

 

Course Objectives

  • To introduce principles of cloud computing and build applications on cloud platforms.
  • To understand distributed computing paradigms and its implementations on cloud platform.
  • To apply principles of Big Query for handling big data.

 

Course Outcomes

 

COs

Description

CO1

To introduce principles of cloud computing.

CO2

Develop and deploying applications on cloud platform.

CO3

Understand Distributed Machine learning with hadoop and spark.

CO4

Create data analytics applications on distributed cloud computing platforms for using Spark and its Tools.

CO5

Develop methods to handle Containers and Kubernetes in Google Cloud.

 

Prerequisites

  • Basics of Machine Learning.

CO-PO Mapping

 

COs

Description

PO1

PO2

PO3

PO4

PO5

CO1

To introduce principles of cloud computing.

2

1

–

–

–

CO2

Develop and deploying applications on cloud platform.

2

3

–

–

–

CO3

Understand Distributed Machine learning with hadoop and spark.

2

3

3

–

–

CO4

Create data analytics applications on distributed cloud computing platforms for using Spark and its Tools.

2

3

3

2

1

CO5

Develop methods to handle Containers and Kubernetes in Google Cloud.

2

3

3

2

1

Evaluation Pattern

Evaluation Pattern – 70:30

 

  • Midterm Exam – 20%
  • Lab Assignments – 25%
  • Project – 25%
  • End Semester Exam – 30%

Text Books / References

Text Book / References

  1. Kai Hwang, “Cloud Computing for Machine Learning and Cognitive Applications”, MIT Press, 2017.
  2. Murari Ramuka, “Data Analytics with Google Cloud Platform “, BPB PUBN, 2019.
  3. Anand Deshpande, Manish Kumar, Vikram Chaudhari, “Hands-On Artificial Intelligence on Google Cloud Platform”, Packt Publishing, 2020.
  4. Jeffrey Jackovich, Ruze Richards, “Machine Learning with AWS”, Packt Publishing, 2018.
  5. Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, “Learning Spark Lightning fast data analysis”, O’Reilly Media, Inc, 2020.

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