AI

Batch write and discover records in Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It provides low-latency online serving for real-time inference, an offline store for historical retention and training feature data, and supports both streaming and batch ingestion patterns. As ML platforms mature, two operational gaps surface

Batch write and discover records in Amazon SageMaker Feature Store Read More »

How Decathlon runs demand forecasting at scale with Chronos-2

This post is co-written with Vianney Bruned, Filippo Giruzzi, Belkiss Saidi, and Carlos Ramirez from Decathlon. Decathlon is one of the world’s largest sporting goods retailers, with more than 100,000 teammates and 400 million users worldwide. The company relies on accurate demand forecasting at scale to support the availability of the appropriate products in each

How Decathlon runs demand forecasting at scale with Chronos-2 Read More »

Spreading the load: How Salesforce met Multi-AZ HA with SageMaker Inference Components

When Salesforce set out to make Agentforce (Salesforce’s AI foundation for agents) highly available (HA) across multiple Availability Zones (AZs), the team faced a gap. Amazon SageMaker AI Inference Components (ICs) could cut GPU costs, but their default placement didn’t guarantee the Multi-AZ resilience Salesforce’s compliance bar required. For Salesforce, the ICs delivered an 8x

Spreading the load: How Salesforce met Multi-AZ HA with SageMaker Inference Components Read More »