Cloud Native Machine Learning
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
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Cloud Native Machine Learning
- 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.