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Build and deploy ML inference applications from scratch using Amazon SageMaker

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers …

HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation. However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly on implicit neural fields in …

Unleashing the power of Presto: The Uber case study

The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. We all recognize it as the platform that connects riders with drivers for hassle-free transportation. But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every …

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Innovation for Inclusion: Hack.The.Bias with Amazon SageMaker

This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove …