Authors :
Ayesha Umaima
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3X0SqpI
DOI :
https://doi.org/10.5281/zenodo.7505018
Abstract :
Machine learning (ML) uses Algorithms to
find patterns in data, learn from those patterns, and build
mathematical models to make predictions about future
data. Through improved disease detection, environmental
protection, the transformation of products and services,
and other factors, these Machine Learning solutions have
the potential to transform lives.
This whitepaper gives you a list of tried-and-true
best practises that are independent of technology and the
cloud. When creating your ML workloads, you can use
these guidelines and architectural principles, or you can
employ them to make ongoing improvements after your
workloads have gone into production.
Machine learning (ML) uses Algorithms to
find patterns in data, learn from those patterns, and build
mathematical models to make predictions about future
data. Through improved disease detection, environmental
protection, the transformation of products and services,
and other factors, these Machine Learning solutions have
the potential to transform lives.
This whitepaper gives you a list of tried-and-true
best practises that are independent of technology and the
cloud. When creating your ML workloads, you can use
these guidelines and architectural principles, or you can
employ them to make ongoing improvements after your
workloads have gone into production.
