Category: Amazon SageMaker

The process of extracting, cleaning, manipulating, and encoding data from raw sources and preparing it to be consumed by machine learning (ML) algorithms is an important, expensive, and time-consuming part of data science. Managing these data pipelines for either training or inference is a challenge for data science teams, however, and can take valuable time […]

Machine learning (ML) models have been deployed successfully across a variety of use cases and industries, but due to the high computational complexity of recent ML models such as deep neural networks, inference deployments have been limited by performance and cost constraints. To add to the challenge, preparing a model for inference involves packaging the […]

For data scientists and machine learning (ML) developers, data preparation is one of the most challenging and time-consuming tasks of building ML solutions. In an often iterative and highly manual process, data must be sourced, analyzed, cleaned, and enriched before it can be used to train an ML model. Typical tasks associated with data preparation […]

Machine learning (ML) is realized in inference. The business problem you want your ML model to solve is the inferences or predictions that you want your model to generate. Deployment is the stage in which a model, after being trained, is ready to accept inference requests. In this post, we describe the parameters that you […]

This is the second in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1: Automate multi-modality, parallel data labeling workflows with Amazon SageMaker Ground Truth and AWS Step Functions, we looked at how to create multi-step labeling workflows for hierarchical label taxonomies using AWS Step Functions. In […]

Neural networks have proven effective at solving complex computer vision tasks such as object detection, image similarity, and classification. With the evolution of low-cost GPUs, the computational cost of building and deploying a neural network has drastically reduced. However, most techniques are designed to handle pixel resolutions commonly found in visual media. For example, typical […]

This post is co-written with Liam Pearson, a Data Scientist at Genworth Mortgage Insurance Australia Limited. Genworth Mortgage Insurance Australia Limited is a leading provider of lenders mortgage insurance (LMI) in Australia; their shares are traded on Australian Stock Exchange as ASX: GMA. Genworth Mortgage Insurance Australia Limited is a lenders mortgage insurer with over […]

This is the first in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1, we look at creating multi-step labeling workflows for hierarchical label taxonomies using AWS Step Functions. In Part 2 (coming soon), we look at how to build dashboards for analyzing dataset annotations and worker […]

Amazon SageMaker hyperparameter tuning provides a built-in solution for scalable training and hyperparameter optimization (HPO). However, for some applications (such as those with a preference of different HPO libraries or customized HPO features), we need custom machine learning (ML) solutions that allow retraining and HPO. This post offers a step-by-step guide to build a custom deep […]

Autonomous vehicle companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment. The LiDAR sensor output is a sequence of 3D point cloud frames (the typical capture rate is […]