MLOps Engineering at Scale

English | 2022 | ISBN: 978-1617297762 | 344 Pages | PDF, EPUB, MOBI | 19 MB

Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools!

In MLOps Engineering at Scale you will learn:

• Extracting, transforming, and loading datasets
• Querying datasets with SQL
• Understanding automatic differentiation in PyTorch
• Deploying model training pipelines as a service endpoint
• Monitoring and managing your pipeline’s life cycle
• Measuring performance improvements

MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities.

A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms.

MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production.

What’s inside

• Reduce or eliminate ML infrastructure management
• Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow
• Deploy training pipelines as a service endpoint
• Monitor and manage your pipeline’s life cycle
• Measure performance improvements