Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

Using active learning to reduce labeling time

Now that we've set up a labeling workflow, we need to think about scale. If our dataset has more than 5,000 records, it's likely that Ground Truth can learn how to label for us. (You need at least 1,250 labeled records for automatic labeling, but at least 5,000 is a good rule of thumb.) This happens in an iterative process, as shown in the following diagram:

Figure 3.4 – Auto-labeling workflow

When you create a labeling job using automatic labeling, Ground Truth will select a random sample of input data for manual labeling. If at least 90% of these items are labeled without error, Ground Truth will split the labeled data into a training and validation set. It will train a model and compute a confidence score, then attempt to label the remaining data. If the automatically generated labels are beneath the confidence threshold, it will refer them to workers for human review. This process repeats until...