AWS Machine Learning Blog
Category: Customer Enablement
How to run an AI powered musical challenge: “AWS DeepComposer Got Talent”
Support for AWS DeepComposer will be ending soon. Please see Support for AWS DeepComposer ending soon for more details. July 2023: This post was reviewed for accuracy. To help you fast track your company’s adoption of machine learning (ML), AWS offers educational solutions for developers to get hands-on experience. We like to think of these […]
Edelweiss improves cross-sell using machine learning on Amazon SageMaker
This post is co-written by Nikunj Agarwal, lead data scientist at Edelweiss Tokio Life Insurance. Edelweiss Tokio Life Insurance Company Ltd is a leading life insurance company in India. Its broad spectrum of offerings includes life insurance, health insurance, retirement policies, wealth enhancement schemes, education funding, and more. How are you being recommended a credit […]
Enhance sports narratives with natural language generation using Amazon SageMaker
This blog post was co-authored by Arbi Tamrazian, Director of Data Science and Machine Learning at Fox Sports. FOX Sports is the sports television arm of FOX Network. The company used machine learning (ML) and Amazon SageMaker to streamline the production of relevant in-game storylines for commentators to use during live broadcasts. “We collaborated with […]
Build BI dashboards for your Amazon SageMaker Ground Truth labels and worker metadata
This is the second in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1: Automate multi-modality, parallel data labeling workflows with Amazon SageMaker Ground Truth and AWS Step Functions, we looked at how to create multi-step labeling workflows for hierarchical label taxonomies using AWS Step Functions. In […]
Build a scalable machine learning pipeline for ultra-high resolution medical images using Amazon SageMaker
Neural networks have proven effective at solving complex computer vision tasks such as object detection, image similarity, and classification. With the evolution of low-cost GPUs, the computational cost of building and deploying a neural network has drastically reduced. However, most techniques are designed to handle pixel resolutions commonly found in visual media. For example, typical […]
Improve the streaming transcription experience with Amazon Transcribe partial results stabilization
Whether you’re watching a live broadcast of your favorite soccer team, having a video chat with a vendor, or calling your bank about a loan payment, streaming speech content is everywhere. You can apply a streaming transcription service to generate subtitles for content understanding and accessibility, to create metadata to enable search, or to extract […]
Automate multi-modality, parallel data labeling workflows with Amazon SageMaker Ground Truth and AWS Step Functions
This is the first in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1, we look at creating multi-step labeling workflows for hierarchical label taxonomies using AWS Step Functions. In Part 2 (coming soon), we look at how to build dashboards for analyzing dataset annotations and worker […]
Annotate dense point cloud data using Amazon SageMaker Ground Truth
Autonomous vehicle companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment. The LiDAR sensor output is a sequence of 3D point cloud frames (the typical capture rate is […]
Quality Assessment for SageMaker Ground Truth Video Object Tracking Annotations using Statistical Analysis
Data quality is an important topic for virtually all teams and systems deriving insights from data, especially teams and systems using machine learning (ML) models. Supervised ML is the task of learning a function that maps an input to an output based on examples of input-output pairs. For a supervised ML algorithm to effectively learn […]
HawkEye 360 uses Amazon SageMaker Autopilot to streamline machine learning model development for maritime vessel risk assessment
This post is cowritten by Ian Avilez and Tim Pavlick from HawkEye 360. HawkEye 360 is a commercial radio frequency (RF) satellite constellation data analytics provider. Our signals of interest include very high frequency (VHF) push-to-talk radios, maritime radar systems, AIS beacons, satellite mobile comms, and more. Our Mission Space offering, released in February 2021, […]