Tag: Amazon SageMaker

Amazon SageMaker is a fully managed service that supports all of the steps of a ML model’s development: data exploration and building, training, and deploying ML models. With Amazon SageMaker, you can pick and use any of the built-in algorithms, reducing the time to market and the development cost.

December 2022: This post was reviewed and updated for accuracy. At AWS Machine Learning (ML) workshops, customers often ask, “After I deploy an endpoint, where do I go from there?” You can deploy an Amazon SageMaker trained and validated ML model as an online endpoint in production. Alternatively, you can choose which SageMaker functionality to […]

Today, we are launching several new features for the Amazon SageMaker BlazingText algorithm. Many downstream natural language processing (NLP) tasks like sentiment analysis, named entity recognition, and machine translation require the text data to be converted into real-valued vectors. Customers have been using BlazingText’s highly optimized implementation of the Word2Vec algorithm, for learning these vectors from […]

Amazon SageMaker is a fully-managed and highly scalable machine learning (ML) platform that makes it easy build, train, and deploy machine learning models. This is a giant step towards the democratization of ML and in lowering the bar for entry in to the ML space for developers. Computer vision is the branch of machine learning […]

Amazon SageMaker is a fully managed service for scalable training and hosting of machine learning models. We’re adding multiclass classification support to the linear learner algorithm in Amazon SageMaker. Linear learner already provides convenient APIs for linear models such as logistic regression for ad click prediction, fraud detection, or other classification problems, and linear regression […]

Today we are launching several new features for DeepAR in Amazon SageMaker. DeepAR is a supervised machine learning algorithm for time series prediction, or forecasting, that uses recurrent neural networks (RNNs) to produce probabilistic forecasts. Since its launch, the algorithm has been used for a variety of use cases. We are excited to give developers access to new […]

We’re excited to announce that starting today Amazon SageMaker supports a built-in k-Nearest-Neighbor (kNN) algorithm for solving classification and regression problems. kNN is a simple, interpretable, and surprisingly strong model for multi-class classification, ranking, and regression. Introduction to kNN The idea behind kNN is that similar data points should have the same class, at least […]

The Discover Financial Services (DFS) team members who attended AWS re:Invent agreed that the Robocar Rally was an extremely impactful experience. By participating in this hackathon, six members of Discover’s core team received hands-on experience using machine learning (ML) and deep learning on AWS. They had a blast and created lasting memories! Discover’s Cloud Center […]

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 […]