Mar 2, 2022 In Finance
Create a Wine Recommender Using NLP on AWS
In this tutorial, we’ll build a simple machine learning pipeline using a BERT word embedding model and the Nearest Neighbor algorithm to recommend wines based on user inputted preferences. To create and power this recommendation engine, we’ll leverage AWS’s SageMaker platform, which provides a fully managed way for us to…
Feb 24, 2022 In Advertising & Marketing, Retail
Amazon Ads Uses PyTorch and AWS Inferentia to Scale Models for Ads Processing
Amazon Ads uses PyTorch, TorchServe, and AWS Inferentia to reduce inference costs by 71% and drive scale out. Amazon Ads helps companies build their brand and connect with shoppers through ads shown both within and beyond Amazon’s store, including websites, apps, and streaming TV content in more than 15 countries.…
Feb 10, 2022 In Healthcare, Research
ChemicalX: A Deep Learning Library for Drug Pair Scoring
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring models accessible to machine learning researchers and practitioners…
The Why and How of Scaling Large Language Models
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Over the past decade, the amount of compute used for the largest training runs has increased at an exponential pace. We’ve also seen in many domains that larger models are able to…
Nov 21, 2021 In Media & Entertainment
Running BERT model inference on AWS Inf1: From model compilation to speed comparison
In this tech blog, we will compare the speed and cost of Inferentia, GPU, and CPU for a BERT sequence labeling example. We also provide a helpful tutorial on the steps for model compilation and inference on Inf1 instances.
Nov 9, 2021 In Research
SearchSage: Learning Search Query Representations at Pinterest
Pinterest surfaces billions of ideas to people every day, and the neural modeling of embeddings for content, users, and search queries are key in the constant improvement of these machine learning-powered recommendations. Good embeddings — representations of discrete entities as vectors of numbers — enable fast candidate generation and are…
Oct 18, 2021 In Retail
How We Built: An Early-Stage Recommender System
Personalization is ubiquitous on most platforms today. Supercharged by connectivity, and scaled by machine learning, most experiences on the internet are tailored to our personal tastes. Peloton classes offer a diversity of instructors, languages, fitness disciplines, durations and intensity. Each Member has specific fitness goals, schedule, fitness equipment, and level…
Sep 7, 2021 In Medical
How AI is Helping Vets to Help our Pets
1 in 4 dogs, and 1 in 5 cats, will develop cancer at some point in their lives. Pets today have a better chance of being successfully treated than ever, thanks to advances in early recognition, diagnosis and treatment.
Using a Grapheme to Phoneme Model in Cisco’s Webex Assistant
Grapheme to Phoneme (G2P) is a function that generates pronunciations (phonemes) for words based on their written form (graphemes). It has an important role in automatic speech recognition systems, natural language processing, and text-to-speech engines. In Cisco’s Webex Assistant, we use G2P modelling to assist in resolving person names from…
University of Pécs enables text and speech processing in Hungarian, builds the BERT-large model with just 1,000 euro with Azure
Everyone prefers to use their mother tongue when communicating with chat agents and other automated services. However, for languages like Hungarian—spoken by only 15 million people—the market size will often be viewed as too small for large companies to create software, tools or applications that can process Hungarian text as…