Cloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You’ll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you’ll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system. Learn more about the book here: http://mng.bz/em9w

1. Leverage cloud-
based machine
learning services
Take 40% off Cloud Native Machine
Learning by entering slosipov into the
discount code box at checkout at
manning.com.

2. Cloud native machine learning tools eliminate the time-consuming
operations tasks from your machine learning lifecycle, letting out-of-
the-box cloud services take over launching, running, and managing
your ML systems. This lets you put your model into production rather
than spending all your time setting up infrastructure.

3. Cloud Native Machine
Learning is a guide to
bringing your experimental
machine learning code to
production using serverless
capabilities from major
cloud providers.
When you’re done, you’ll
have the tools to easily
bridge the gap between ML
models and a fully
functioning production
system.

4. Cloud Native Machine Learning helps you bridge that gap by using the
pre-built services provided by cloud platforms like Azure and AWS to
assemble your ML system’s infrastructure.
In it, you’ll learn how to develop reliable, flexible, and scalable machine
learning systems without time-consuming management tasks or the
costly overheads of physical hardware.

5. What people are saying
about the book:
If you want to avoid
painful infrastructure
hassles and costs,
read this book and
hop onto the
serverless
ecosystem!
-Daniela Zapata
It's clear the author
has street cred and
has done quality
work in the trenches.
-Todd Cook
If you're trying to get
started with running
ML projects on AWS,
this is the book for
you! The book has a
good pace, and is
easy to follow.
-Mathijs Affourtit

6. About the author:
Carl Osipov has spent over 15 years
working on big data processing and
machine learning in multi-core,
distributed systems. While at IBM, Carl
helped IBM Software Group to shape its
strategy around the use of Docker and
other container-based technologies for
serverless computing using IBM Cloud
and Amazon Web Services. At Google,
Carl learned from the world’s foremost
experts in machine learning. You can
learn more about Carl from his blog
Clouds With Carl.

7. Take 40% off Cloud Native Machine
Learning by entering slosipov into the
discount code box at checkout at
manning.com.
You can preview the book’s contents on
our browser-based liveBook platform
here.