The document discusses machine learning in relation to cloud technologies and architecture. It covers cloud computing service models and deployment models with machine learning. It also lists advantages like starting ML projects without significant upfront costs, accessing high-performance hardware resources without purchasing them, and quickly scaling ML capabilities as projects grow. Disadvantages include dependence on experts, challenges with data mobility, and security risks.

1. Cloud Technology and
Architecture
Sudi Murindanyi
2022/HD05/5583X
Individual Assignment

2. A Comprehensive Review of Machine Learning in Relation
to Cloud Technologies and Architecture

3. Cloud Computing Service Models with Machine Learning

4. Cloud Computing Deployment Models With ML

5. Advantages and Disadvantages of Utilizing Machine
Learning In the Cloud
Advantages
● Thanks to on-demand pricing models, the ability to
start ML projects without a significant initial
investment.
● Access to high-performance resources like GPUs
and FPGAs without investing in hardware.
● The ability to quickly experiment and scale
machine learning capabilities as projects move
into production and demand increases.
● The ability to utilise ML capabilities without
extensive artificial intelligence or data science
knowledge.
Disadvantages
● Dependence on Experts
● Data Mobility
● Security Risks