AWS Machine Learning Blog
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 […]
How lekker got more insights into their customer churn model with Amazon SageMaker Debugger
With over 400,000 customers, lekker Energie GmbH is a leading supraregional provider of electricity and gas on the German energy market. lekker is customer and service oriented and regularly scores top marks in comparison tests. As one of the most important suppliers of green electricity to private households, the company, with its 220 employees, stands […]
Best practices in customer service automation
Chatbots, virtual assistants, and Interactive Voice Response (IVR) systems are key components of successful customer service strategies. We had the pleasure of hearing from three AWS Contact Center Intelligence (AWS CCI) Partners as part of our Best Practices in Customer Service Automation webinar, who provided valuable insights and tips for building automated, customer-service solutions. The […]
Implement live customer service chat with two-way translation, using Amazon Connect and Amazon Translate
November 2023: This post was reviewed and updated for accuracy. Many businesses support customers across multiple countries and ethnic communities, and therefore need to provide customer service in a wide variety of local languages. It’s hard to consistently staff contact centers with agents with different language proficiencies. During periods of high call volumes, callers often […]
Reduce ML inference costs on Amazon SageMaker with hardware and software acceleration
Amazon SageMaker is a fully-managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at 50% lower TCO than self-managed deployments on Elastic Compute Cloud (Amazon EC2). Elastic Inference is a capability of SageMaker that delivers 20% better performance for model inference than AWS Deep Learning Containers on […]
Automate feature engineering pipelines with Amazon SageMaker
The process of extracting, cleaning, manipulating, and encoding data from raw sources and preparing it to be consumed by machine learning (ML) algorithms is an important, expensive, and time-consuming part of data science. Managing these data pipelines for either training or inference is a challenge for data science teams, however, and can take valuable time […]
Learn how the winner of the AWS DeepComposer Chartbusters Keep Calm and Model On challenge used Transformer algorithms to create music
AWS is excited to announce the winner of the AWS DeepComposer Chartbusters Keep Calm and Model On challenge, Nari Koizumi. AWS DeepComposer gives developers a creative way to get started with machine learning (ML) by creating an original piece of music in collaboration with artificial intelligence (AI). In June 2020, we launched Chartbusters, a global […]
Speed up YOLOv4 inference to twice as fast on Amazon SageMaker
Machine learning (ML) models have been deployed successfully across a variety of use cases and industries, but due to the high computational complexity of recent ML models such as deep neural networks, inference deployments have been limited by performance and cost constraints. To add to the challenge, preparing a model for inference involves packaging the […]
Amazon Lookout for Vision Accelerator Proof of Concept (PoC) Kit
Amazon Lookout for Vision is a machine learning service that spots defects and anomalies in visual representations using computer vision. With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Basler and Amazon Lookout for Vision have collaborated to launch the “Amazon […]
Prepare data for predicting credit risk using Amazon SageMaker Data Wrangler and Amazon SageMaker Clarify
For data scientists and machine learning (ML) developers, data preparation is one of the most challenging and time-consuming tasks of building ML solutions. In an often iterative and highly manual process, data must be sourced, analyzed, cleaned, and enriched before it can be used to train an ML model. Typical tasks associated with data preparation […]