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

Improving labeling quality using multiple workers

Relying on a single opinion for a subjective evaluation is risky. In some cases, labeling seems straightforward; telling a car from an airplane when labeling transportation pictures is pretty simple. But let's go back to our weather data. If we're labeling air quality as good or bad based on a measurement that's not intuitive, such as the level of particulate matter (PM25), we may find that a worker's opinion depends greatly on the advice we give them and their preconceptions. If a worker believes that a certain city or country has dirty air, they are likely to favor a bad label in ambiguous cases. And these biases have real consequences – some governments are very sensitive to the idea that their air quality is bad!

One way to combat this problem is to use multiple workers to label each item and somehow combine the scores. In the notebook section called Add another worker, we'll add a second worker...