AWS Machine Learning Blog

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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. […]

The machine learning (ML) model-building process requires data scientists to manually prepare data features, select an appropriate algorithm, and optimize its model parameters. It involves a lot of effort and expertise. Amazon SageMaker Autopilot removes the heavy lifting required by this ML process. It inspects your dataset, generates several ML pipelines, and compares their performance […]

There are many techniques to train deep learning models with a small amount of data. Examples include transfer learning, few-shot learning, or even one-shot learning for an image classification task and fine-tuning for language models based on a pre-trained BERT or GPT2 model. However, you may still have a use case in which you need […]