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
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CO Description
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CO1
|
Understand the basic principles of cloud computing
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CO2
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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.
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CO3
|
Apply the cloud big data analysis framework to capture, manage, and process real-time data.
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CO4
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Apply cloud Artificial Intelligence platform and cloud cognitive services to build, deploy, and manage
machine learning models.
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CO5
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Understand and apply Cloud dataflow models
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CO-PO Mapping
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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.