AWS Machine Learning Blog

Category: Artificial Intelligence

Automate the customer service experience for flight reservations using Amazon Lex

As air travel starts to pick up in many parts of the world, digitization continues to transform the aviation industry. Airlines are working to reduce the number of touchpoints at the airport. Best practices have been implemented to minimize the number of physical interactions between employees and travelers. As a result, customer service is undergoing […]

Build conversational experiences for auto insurance using Amazon Lex

Auto insurance companies are focusing on digital innovations to meet customer needs. Digital-first engagements provide tailored coverage, transparent information, and seamless experiences. The shift to virtual channels for customer service that occurred during the pandemic is unlikely to revert to traditional channels for many customers. The change in consumer behavior continues to accelerate due to […]

How TourRadar automates the translation process using Amazon EventBridge and Amazon Translate

This is a guest post written by Gergely Kadi, Senior Systems Engineer and Martin Petraschek-Stummer, Senior Data Engineer at TourRadar. TourRadar is a travel marketplace to connect people to life-enriching travel experiences. When it was launched, TourRadar only offered tours and content in English. As the company grew, we saw an opportunity to expand our […]

How accelerates property-based ML model delivery with Amazon SageMaker

This post was created in collaboration with Mohammed Alauddin, Data Engineering and Data Science Regional Manager, and Kamal Hossain, Lead Data Scientist at, now part of PropertyGuru Group. is the market-leading property portal in Malaysia and is now part of the PropertyGuru Group. offers a search experience that enables property seekers to […]

Enhance your machine learning development by using a modular architecture with Amazon SageMaker projects

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost […]

Onboard OneLogin SSO users to Amazon SageMaker Studio

Amazon SageMaker is a fully managed service that provides every machine learning (ML) developer and data scientist the ability to build, train, and deploy ML models at scale. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for ML. Amazon SageMaker Studio provides all the tools you need to take your models from experimentation […]

Optimize your budget and time by submitting Amazon Polly voice synthesis tasks in bulk

Amazon Polly is a service that turns text into natural-sounding speech, using dozens of voices in more than 30 languages. You can use it for all sorts of applications, ranging from talking animated avatars, to lifelike virtual agents that answer customer support requests, to automated newscasters reading stories aloud. You can have Amazon Polly return […]

Build Custom SageMaker Project Templates – Best Practices

SageMaker Projects give organizations the ability to easily setup and standardize developer environments for data scientists and CI/CD systems for MLOps Engineers. With SageMaker Projects, MLOps engineers or organization admins can define templates which bootstrap the ML Workflow with source version control, automated ML Pipelines, and a set of code to quickly start iterating over […]

Train models faster with an automated data profiler for Amazon Fraud Detector

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Amazon Fraud Detector uses machine learning (ML) under the hood and is based on over 20 years of fraud detection expertise from Amazon. It automatically identifies […]

Extend model lineage to include ML features using Amazon SageMaker Feature Store

Feature engineering is expensive and time-consuming, which may lead you to adopt a feature store for managing features across teams and models. Unfortunately, machine learning (ML) lineage solutions have yet to adapt to this new concept of feature management. To achieve the full benefits of a feature store by enabling feature reuse, you need to […]