AWS Machine Learning Blog

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 […]

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 […]

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 […]

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 […]

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 […]

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 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 […]

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 […]

Machine learning (ML) is realized in inference. The business problem you want your ML model to solve is the inferences or predictions that you want your model to generate. Deployment is the stage in which a model, after being trained, is ready to accept inference requests. In this post, we describe the parameters that you […]

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 […]