AWS Machine Learning Blog

Category: Artificial Intelligence

Bundesliga Match Fact Win Probability: Quantifying the effect of in-game events on winning chances using machine learning on AWS

Ten years from now, the technological fitness of clubs will be a key contributor towards their success. Today we’re already witnessing the potential of technology to revolutionize the understanding of football. xGoals quantifies and allows comparison of goal scoring potential of any shooting situation, while xThreat and EPV models predict the value of any in-game […]

Unified data preparation, model training, and deployment with Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot – Part 2

Depending on the quality and complexity of data, data scientists spend between 45–80% of their time on data preparation tasks. This implies that data preparation and cleansing take valuable time away from real data science work. After a machine learning (ML) model is trained with prepared data and readied for deployment, data scientists must often […]

How Sophos trains a powerful, lightweight PDF malware detector at ultra scale with Amazon SageMaker

This post is co-authored by Salma Taoufiq and Harini Kannan from Sophos. As a leader in next-generation cybersecurity, Sophos strives to protect more than 500,000 organizations and millions of customers across over 150 countries against evolving threats. Powered by threat intelligence, machine learning (ML), and artificial intelligence from Sophos X-Ops, Sophos delivers a broad and […]

Build an AI-powered virtual agent for Genesys Cloud using QnABot and Amazon Lex

The rise of artificial intelligence technologies enables organizations to adopt and improve self-service capabilities in contact center operations to create a more proactive, timely, and effective customer experience. Voice bots, or conversational interactive voice response systems (IVR), use natural language processing (NLP) to understand customers’ questions and provide relevant answers. Businesses can automate responses to […]

Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker

As businesses and IT leaders look to accelerate the adoption of machine learning (ML), there is a growing need to understand spend and cost allocation for your ML environment to meet enterprise requirements. Without proper cost management and governance, your ML spend may lead to surprises in your monthly AWS bill. Amazon SageMaker is a […]

Index your Dropbox content using the Dropbox connector for Amazon Kendra

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should […]

Provision and manage ML environments with Amazon SageMaker Canvas using AWS CDK and AWS Service Catalog

November 2022: This post was reviewed and updated with new functionality in Amazon SageMaker Canvas that supports tags to track and allocate costs incurred by users. The proliferation of machine learning (ML) across a wide range of use cases is becoming prevalent in every industry. However, this outpaces the increase in the number of ML […]

New features for Amazon SageMaker Pipelines and the Amazon SageMaker SDK

Amazon SageMaker Pipelines allows data scientists and machine learning (ML) engineers to automate training workflows, which helps you create a repeatable process to orchestrate model development steps for rapid experimentation and model retraining. You can automate the entire model build workflow, including data preparation, feature engineering, model training, model tuning, and model validation, and catalog […]

Reduce the time taken to deploy your models to Amazon SageMaker for testing

Data scientists often train their models locally and look for a proper hosting service to deploy their models. Unfortunately, there’s no one set mechanism or guide to deploying pre-trained models to the cloud. In this post, we look at deploying trained models to Amazon SageMaker hosting to reduce your deployment time. SageMaker is a fully […]

Introducing self-service quota management and higher default service quotas for Amazon Textract

Today, we’re excited to announce self-service quota management support for Amazon Textract via the AWS Service Quotas console, and higher default service quotas in select AWS Regions. Customers tell us they need quick turnaround times to process their requests for quota increases and visibility into their service quotas so they may continue to scale their […]