Delivering machine learning (ML) solutions to production is difficult. It’s not easy to know where to start, which tools and techniques to use, and whether you’re doing it right. ML professionals use different techniques based on their individual experiences, or they use prescribed tools that were developed within their company. In either case, deciding what to do, implementing the solution, and maintaining it require significant investments in time and resources. Although existing ML techniques help speed up parts of the process, integrating these techniques to deliver robust solutions requires months of work. This guide is the first part of a content series that focuses on machine learning and provides examples of how you can get started quickly. The goal of the series is to help you standardize your ML approach, make design decisions, and deliver your ML solutions efficiently. We will be publishing additional ML guides in the coming months, so please check the AWS Prescriptive Guidance website for updates.

This guide explores current techniques for quantifying and managing uncertainty in deep learning systems, to improve predictive modeling in ML solutions. This content is for data scientists, data engineers, software engineers, and data science leaders who are looking to deliver high-quality, production-ready ML solutions efficiently and at scale. The information is relevant for data scientists regardless of their cloud environment or the Amazon Web Services (AWS) services they are using or are planning to use.

This guide assumes familiarity with introductory concepts in probability and deep learning. For suggestions on building machine learning competency at your organization, see Deep Learning Specialization on the Coursera website, or the resources on the Machine Learning: Data Scientist page on the AWS Training and Certification website.

https://docs.aws.amazon.com/prescriptive-guidance/latest/ml-quantifying-uncertainty/welcome.html