AWS Machine Learning Blog

Category: Artificial Intelligence

How Süddeutsche Zeitung optimized their audio narration process with Amazon Polly

This is a guest post by Jakob Kohl, a Software Developer at the Süddeutsche Zeitung. Süddeutsche Zeitung is one of the leading quality dailies in Germany when it comes to paid subscriptions and unique users. Its website, SZ.de, reaches more than 15 million monthly unique users as of October 2021. Thanks to smart speakers and […]

Normalize datasets used to train machine learning model

Reduce costs and complexity of ML preprocessing with Amazon S3 Object Lambda

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. Often, customers have objects in S3 buckets that need further processing to be used effectively by consuming applications. Data engineers must support these application-specific data views with trade-offs between persisting derived copies or transforming data […]

Extract entities from insurance documents using Amazon Comprehend named entity recognition

Intelligent document processing (IDP) is a common use case for customers on AWS. You can utilize Amazon Comprehend and Amazon Textract for a variety of use cases ranging from document extraction, data classification, and entity extraction. One specific industry that uses IDP is insurance. They use IDP to automate data extraction for common use cases such as claims intake, […]

Implement MLOps using AWS pre-trained AI Services with AWS Organizations

The AWS Machine Learning Operations (MLOps) framework is an iterative and repetitive process for evolving AI models over time. Like DevOps, practitioners gain efficiencies promoting their artifacts through various environments (such as quality assurance, integration, and production) for quality control. In parallel, customers rapidly adopt multi-account strategies through AWS Organizations and AWS Control Tower to […]

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

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 combine features without having to write any code. […]

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

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 your data processing pipeline. Data Wrangler offers export […]

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 […]