Category: Amazon SageMaker

Amazon SageMaker now supports Amazon Virtual Private Cloud (VPC) Endpoints via AWS PrivateLink so you can initiate prediction calls to your machine learning models hosted on Amazon SageMaker inside your VPC, without going over the internet. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning […]

Principal Component Analysis (PCA) is a very popular technique used by data scientists primarily for dimensionality reduction in numerous applications ranging from stock market prediction to medical image classification. Other uses of PCA include de-noising and feature extraction. PCA is also used as an exploratory data analysis tool. To better understand PCA let’s consider an […]

Amazon SageMaker provides a powerful platform for building, training, and deploying machine learning models into a production environment on AWS. By combining this powerful platform with the serverless capabilities of Amazon Simple Storage Service (S3), Amazon API Gateway, and AWS Lambda, it’s possible to transform an Amazon SageMaker endpoint into a web application that accepts […]

Structured and unstructured data are being generated at an unprecedented rate, so you need the right tools to help organize, search, and understand this vast amount of information, it’s challenging to make the data useful. This is especially true for unstructured data, and it’s estimated that over 80% of the data in enterprises is unstructured. Text analytics […]

January 2021: Post updated with changes required for SageMaker SDK v2, courtesy of Eitan Sela, Senior Startup Solutions Architect Machine learning is a powerful tool that has recently enabled use cases that were never previously possible–computer vision, self-driving cars, natural language processing, and more. Machine learning is a promising technology, but it can be complex […]

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer.  Just like with those frameworks, now you can write your PyTorch script like you normally would and […]

Factorization machines can be used for classification or regression and are much more computationally efficient on large sparse data sets than traditional algorithms like linear regression. This property is why FM are widely used for recommendation.

August 2021: Post updated with changes required for SageMaker SDK v2, courtesy of Eitan Sela, Senior Startup Solutions Architect In the United States, with the 2018 midterm elections approaching, people are looking for more information about the voting process. This blog post explores how we can apply machine learning (ML) to better integrate science into […]

We’re the AWS Marketing Data Science team. We use advanced analytical and machine learning (ML) techniques so we can share insights into business problems across the AWS customer lifecycle, such as ML-driven scoring of sales leads, ML-based targeting segments, and econometric models for downstream impact measurement. Within Amazon, each team operates independently and owns the […]

Today we are launching the training job cloning feature on the Amazon SageMaker console, which makes it much easier for you to create training jobs based on existing ones. When you use Amazon SageMaker, it’s common to run multiple training jobs using different training sets and identical configuration. It’s also common to adjust a specific […]