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Course Detail

Course Name Cloud Computing
Course Code 24MU645
Program M.Tech. Manufacturing and Automation​
Credits 3
Campus Coimbatore

Syllabus

Unit 1

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. Availability, Mobility, and Cluster Optimization; Cloud machine learning engine – cloud MLE train/deploy process, running single instance training and distributed training, hyper parameter tuning, Making predictions on cloud MLE, Batch prediction.

Unit 2

Data Collection, Mining, and Analytics on Clouds – Data quality control and representations, Data mining and data analytics on cloud, cloud resources for supporting Big data analytics; Cloud AI services – overview, Natural language Processing – Document Classification, summarisation, sentiment analysis, topic modelling and theme extraction, chatbots

Unit 3

Understanding cloud language translation services, Analysing images with computer vision – Detecting objects and themes in images, image moderation, Facial analysis, text in images. Video Intelligence – Label detection, Operation status. Cloud Speech – synchronous and asynchronous Speech recognition, streaming speech recognition. Cloud dataflow – dataflow templates, data transformation with cloud dataflow. cloud publisher subscriber – architecture, message flow, implementation.

Objectives and Outcomes

Course Objectives

  • Introduce on the cloud computing fundamentals, including service and deployment models
  • Make informed decisions when selecting and implementing cloud-based solutions for various projects and
  • Develop practical skills in managing and monitoring cloud deployments, focusing on orchestration, automation, and resource management

 

Course Outcomes

CO

CO Description

CO1

Understand the basic principles of cloud computing

CO2

Apply cloud machine learning platform to train machine learning models at scale, host trained model in the

cloud, and use model to make predictions about new data.

CO3

Apply the cloud big data analysis framework to capture, manage, and process real-time data.

CO4

Apply cloud Artificial Intelligence platform and cloud cognitive services to build, deploy, and manage

machine learning models.

CO5

Understand and apply Cloud dataflow models

 

CO-PO Mapping

 

PO1

PO2

PO3

PO4

PO5

PO6

CO1

3

1

2

 

 

1

CO2

3

1

2

1

 

3

CO3

3

1

2

1

 

3

CO4

3

1

2

1

 

3

CO5

3

1

2

1

 

3

 

Skills Acquired

Cloud store, manage, analyze, and skills required to build intelligent applications; Cloud computing tools and techniques to quickly build prototypes and eventually build applications.

Text Books / References

  1. Kai Hwang, “Cloud Computing for Machine Learning and Cognitive Applications”, The MIT Press,
  2. Ekaba Bisong, “Building Machine Learning and Deep Learning Models on Google Cloud Platform”, Apress,
  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,

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