AWS Machine Learning Blog

Improve high-value research with Hugging Face and Amazon SageMaker asynchronous inference endpoints

Many of our AWS customers provide research, analytics, and business intelligence as a service. This type of research and business intelligence enables their end customers to stay ahead of markets and competitors, identify growth opportunities, and address issues proactively. For example, some of our financial services sector customers do research for equities, hedge funds, and […]

Announcing the launch of the model copy feature for Amazon Comprehend custom models

Technology trends and advancements in digital media in the past decade or so have resulted in the proliferation of text-based data. The potential benefits of mining this text to derive insights, both tactical and strategic, is enormous. This is called natural language processing (NLP). You can use NLP, for example, to analyze your product reviews […]

Balance your data for machine learning with Amazon SageMaker Data Wrangler

August 2023: This post was reviewed for accuracy. Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and […]

Launch processing jobs with a few clicks using Amazon SageMaker Data Wrangler

August 2023: This post was reviewed for accuracy. Amazon SageMaker Data Wrangler makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. Previously, when you created a Data Wrangler data flow, you could choose different export options to easily integrate that data flow into […]

Prepare and analyze JSON and ORC data with Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications via a visual interface. Data preparation is a crucial step of the ML lifecycle, and Data Wrangler provides an end-to-end solution to import, prepare, transform, featurize, and […]

Run AutoML experiments with large parquet datasets using Amazon SageMaker Autopilot

Starting today, you can use Amazon SageMaker Autopilot to tackle regression and classification tasks on large datasets up to 100 GB. Additionally, you can now provide your datasets in either CSV or Apache Parquet content types. Businesses are generating more data than ever. A corresponding demand is growing for generating insights from these large datasets […]

Use a web browser plugin to quickly translate text with Amazon Translate

Web browsers can be a single pane of glass for organizations to interact with their information—all of the tools can be viewed and accessed on one screen so that users don’t have to switch between applications and interfaces. For example, a customer call center might have several different applications to see customer reviews, social media […]

How Clearly accurately predicts fraudulent orders using Amazon Fraud Detector

This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Sarvi Loloei, Machine Learning Engineer at Clearly. The content and opinions in this post are those of the third-party authors and AWS is not responsible for the content or accuracy of this post. A pioneer in online shopping, Clearly launched their first site […]

How Logz.io accelerates ML recommendations and anomaly detection solutions with Amazon SageMaker

Logz.io is an AWS Partner Network (APN) Advanced Technology Partner with AWS Competencies in DevOps, Security, and Data & Analytics. Logz.io offers a software as a service (SaaS) observability platform based on best-in-class open-source software solutions for log, metric, and tracing analytics. Customers are sending an increasing amount of data to Logz.io from various data […]

Detect mitotic figures in whole slide images with Amazon Rekognition

Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. […]