Book Image

Production-Ready Applied Deep Learning

By : Tomasz Palczewski, Jaejun (Brandon) Lee, Lenin Mookiah
Book Image

Production-Ready Applied Deep Learning

By: Tomasz Palczewski, Jaejun (Brandon) Lee, Lenin Mookiah

Overview of this book

Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives. First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors’ collective knowledge of deploying hundreds of AI-based services at a large scale. By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting.
Table of Contents (19 chapters)
1
Part 1 – Building a Minimum Viable Product
6
Part 2 – Building a Fully Featured Product
10
Part 3 – Deployment and Maintenance

Monitoring using CloudWatch

First, we will introduce a few key concepts in CloudWatch: logs, metrics, alarms, and dashboards. CloudWatch persists ingested data in the form of logs or metrics organized by timestamps. As the name suggests, logs refer to text data emitted throughout the lifetime of a program. On the other hand, metrics represent organized numeric data such as CPU or memory utilization. Since metrics are stored in an organized matter, CloudWatch supports aggregating metrics and creating histograms from collected data. An alarm can be set up to alert if unusual changes are reported for the target metric. Also, a dashboard can be set up to get an intuitive view of selected metrics and raised alarms.

In the following example, we will describe how to log metric data using a CloudWatch service client from the boto3 library. The metric data is structured as a dictionary and consists of metric names, dimensions, and values. The idea of dimensions is to capture factual information...