Category: Artificial Intelligence

For customers looking to implement a GxP-compliant environment on AWS for artificial intelligence (AI) and machine learning (ML) systems, we have released a new whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences. This whitepaper provides an overview of security and good ML compliance practices and guidance on building GxP-regulated AI/ML systems using AWS […]

Data science and data engineering teams spend a significant portion of their time in the data preparation phase of a machine learning (ML) lifecycle performing data selection, cleaning, and transformation steps. It’s a necessary and important step of any ML workflow in order to generate meaningful insights and predictions, because bad or low-quality data greatly […]

Today, customers interact with brands over an increasingly large digital and offline footprint, generating a wealth of interaction data known as behavioral data. As a result, marketers and customer experience teams must work with multiple overlapping tools to engage and target those customers across touchpoints. This increases complexity, creates multiple views of each customer, and […]

This is blog post is co-written by Theresa Cabrera Menard, an Applied Scientist/Geographic Information Systems Specialist at The Nature Conservancy (TNC) in Hawaii. In recent years, Amazon and AWS have developed a series of sustainability initiatives with the overall goal of helping preserve the natural environment. As part of these efforts, AWS Professional Services establishes […]

Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for inference. You provide SageMaker Autopilot with a tabular data set and a target attribute to predict. Then, SageMaker Autopilot automatically explores your data, trains, tunes, ranks and finds […]

This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]

Amazon Kendra is an intelligent search service powered by machine learning. You can receive spelling suggestions for misspelled terms in your queries by utilizing the Amazon Kendra Spell Checker. Spell Checker helps reduce the frequency of queries returning irrelevant results by providing spelling suggestions for unrecognized terms. In this post, we explore how to use […]

This is the second post in a two-part series in which I propose a practical guide for organizations so you can assess the quality of text summarization models for your domain. For an introduction to text summarization, an overview of this tutorial, and the steps to create a baseline for our project (also referred to […]

When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested in technology but usually don’t care much about […]

This is a guest post co-authored by Taylor Names, Staff Machine Learning Engineer, Dev Gupta, Machine Learning Manager, and Argie Angeleas, Senior Product Manager at Ibotta. Ibotta is an American technology company that enables users with its desktop and mobile apps to earn cash back on in-store, mobile app, and online purchases with receipt submission, […]