Category: Amazon SageMaker

High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For example, you may want to create […]

Model training and serving steps are two essential pieces of a successful end-to-end machine learning (ML) pipeline. These two steps often require different software and hardware setups to provide the best mix for a production environment. Model training is optimized for a low-cost, feasible total run duration, scientific flexibility, and model interpretability objectives, whereas model […]

Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each month for the latest roundup. Launches […]

Applications based on machine learning (ML) can provide tremendous business value. Using ML, we can solve some of the most complex engineering problems that previously were infeasible. One of the advantages of running ML on the AWS Cloud is that you can continually optimize your workloads and reduce your costs. In this post, we discuss […]

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

The Guinness Six Nations Championship began in 1883 as the Home Nations Championship among England, Ireland, Scotland, and Wales, with the inclusion of France in 1910 and Italy in 2000. It is among the oldest surviving rugby traditions and one of the best-attended sporting events in the world. The COVID-19 outbreak disrupted the end of […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. This post has also been updated with changes required for SageMaker SDK v2 and an improved notebook experience. The rise of semantic search engines has made ecommerce and retail businesses search easier for its consumers. Search engines powered by natural […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Amazon SageMaker Studio Notebooks for exploring datasets and building models. With the new ability to launch Amazon SageMaker Studio in your Amazon Virtual Private Cloud (Amazon VPC), you […]

Businesses are increasingly deploying multiple machine learning (ML) models to serve precise and accurate predictions to their consumers. Consider a media company that wants to provide recommendations to its subscribers. The company may want to employ different custom models for recommending different categories of products—such as movies, books, music, and articles. If the company wants […]

As the volume of unstructured data such as text and voice continues to grow, businesses are increasingly looking for ways to incorporate this data into their time series predictive modeling workflows. One example use case is transcribing calls from call centers to forecast call handle times and improve call volume forecasting. In the retail or […]