Category: Amazon SageMaker

This blog post was co-authored by Arbi Tamrazian, Director of Data Science and Machine Learning at Fox Sports. FOX Sports is the sports television arm of FOX Network. The company used machine learning (ML) and Amazon SageMaker to streamline the production of relevant in-game storylines for commentators to use during live broadcasts. “We collaborated with […]

With over 400,000 customers, lekker Energie GmbH is a leading supraregional provider of electricity and gas on the German energy market. lekker is customer and service oriented and regularly scores top marks in comparison tests. As one of the most important suppliers of green electricity to private households, the company, with its 220 employees, stands […]

Amazon SageMaker is a fully-managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at 50% lower TCO than self-managed deployments on Elastic Compute Cloud (Amazon EC2). Elastic Inference is a capability of SageMaker that delivers 20% better performance for model inference than AWS Deep Learning Containers on […]

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 […]