Build conversational experiences for retail order management using Amazon Lex

Favorite Retailers want to stay engaged with their customers as they move seamlessly between digital channels and physical storefronts. By delivering personalized and consistent experiences across a range of retail touchpoints, companies can drive brand loyalty. Customers contact retailers’ customer support for reasons such as checking order status, updating shipping

Read More
Shared by AWS Machine Learning October 30, 2021

Deliver natural and efficient customer service experiences to mobile subscribers with Amazon Lex

Favorite Mobile service providers manage a high volume of customer service calls daily. Rapidly evolving network technology and device innovations are shaping customer expectations. Delighting callers with a quick interaction is core to a successful customer experience strategy. Mobile subscribers contact customer support for several reasons such as requesting a

Read More
Shared by AWS Machine Learning October 30, 2021

Build Custom SageMaker Project Templates – Best Practices

Favorite 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

Read More
Shared by AWS Machine Learning October 28, 2021

Onboard OneLogin SSO users to Amazon SageMaker Studio

Favorite 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

Read More
Shared by AWS Machine Learning October 28, 2021

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

Favorite 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

Read More
Shared by AWS Machine Learning October 28, 2021