AWS Machine Learning Blog
Category: Artificial Intelligence
Accessing data sources from Amazon SageMaker R kernels
Amazon SageMaker notebooks now support R out-of-the-box, without needing you to manually install R kernels on the instances. Also, the notebooks come pre-installed with the reticulate library, which offers an R interface for the Amazon SageMaker Python SDK and enables you to invoke Python modules from within an R script. You can easily run machine […]
Read MoreTraining a custom single class object detection model with Amazon Rekognition Custom Labels
Customers often need to analyze their images to find objects that are unique to their business needs. In many cases, this may be a single object, like identifying the company’s logo, finding a particular industrial or agricultural defect, or locating a specific event like a hurricane in satellite scans. In this post, we showcase how […]
Read MoreIncreasing the relevance of your Amazon Personalize recommendations by leveraging contextual information
Getting relevant recommendations in front of your users at the right time is a crucial step for the success of your personalization strategy. However, your customer’s decision-making process shifts depending on the context at the time when they’re interacting with your recommendations. In this post, I show you how to set up and query a […]
Read MoreAmazon Forecast can now use Convolutional Neural Networks (CNNs) to train forecasting models up to 2X faster with up to 30% higher accuracy
We’re excited to announce that Amazon Forecast can now use Convolutional Neural Networks (CNNs) to train forecasting models up to 2X faster with up to 30% higher accuracy. CNN algorithms are a class of neural network-based machine learning (ML) algorithms that play a vital role in Amazon.com’s demand forecasting system and enable Amazon.com to predict […]
Read MoreSecuring Amazon Comprehend API calls with AWS PrivateLink
Amazon Comprehend now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints via AWS PrivateLink so you can securely initiate API calls to Amazon Comprehend from within your VPC and avoid using the public internet. Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning (ML) to find meaning and insights […]
Read MoreMachine learning best practices in financial services
We recently published a new whitepaper, Machine Learning Best Practices in Financial Services, that outlines security and model governance considerations for financial institutions building machine learning (ML) workflows. The whitepaper discusses common security and compliance considerations and aims to accompany a hands-on demo and workshop that walks you through an end-to-end example. Although the whitepaper […]
Read MoreBuild more effective conversations on Amazon Lex with confidence scores and increased accuracy
In the rush of our daily lives, we often have conversations that contain ambiguous or incomplete sentences. For example, when talking to a banking associate, a customer might say, “What’s my balance?” This request is ambiguous and it is difficult to disambiguate if the intent of the customer is to check the balance on her […]
Read MoreTraining knowledge graph embeddings at scale with the Deep Graph Library
We’re extremely excited to share the Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph (KG) embeddings library built on top of the Deep Graph Library (DGL). DGL is an easy-to-use, high-performance, scalable Python library for deep learning on graphs. You can now create embeddings for large KGs containing billions of nodes and edges two-to-five […]
Read MoreBuilding a Pictionary-style game with AWS DeepLens and Amazon Alexa
Are you bored of the same old board games? Tired of going through the motions with charades week after week? In need of a fun and exciting way to mix up game night? Well we have a solution for you! From the makers of AWS DeepLens, Guess My Drawing with DeepLens is a do-it-yourself recipe […]
Read MoreSafely deploying and monitoring Amazon SageMaker endpoints with AWS CodePipeline and AWS CodeDeploy
As machine learning (ML) applications become more popular, customers are looking to streamline the process for developing, deploying, and continuously improving models. To reliably increase the frequency and quality of this cycle, customers are turning to ML operations (MLOps), which is the discipline of bringing continuous delivery principles and practices to the data science team. […]
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