AWS Machine Learning Blog

The built-in Amazon SageMaker XGBoost algorithm provides a managed container to run the popular XGBoost machine learning (ML) framework, with added convenience of supporting advanced training or inference features like distributed training, dataset sharding for large-scale datasets, A/B model testing, or multi-model inference endpoints. You can also extend this powerful algorithm to accommodate different requirements. […]

Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your employees based on images of their […]

As more organizations move to machine learning (ML) to drive deeper insights, two key stumbling blocks they run into are labeling and lifecycle management. Labeling is the identification of data and adding labels to provide context so an ML model can learn from it. Labels might indicate a phrase in an audio file, a car […]

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document formats, […]

Customer service calls require customer agents to have the customer’s account information to process the caller’s request. For example, to provide a status on an insurance claim, the support agent needs policy holder information such as the policy ID and claim number. Such information is often collected in the interactive voice response (IVR) flow at […]

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 […]