AWS Machine Learning Blog

Identifying worker labeling efficiency using Amazon SageMaker Ground Truth

A critical success factor in machine learning (ML) is the cleanliness and accuracy of training datesets. Training with mislabeled or inaccurate data can lead to a poorly performing model. But how can you easily determine if the  labeling team is  accurately labeling data? One way is to manually sift through the results one worker at […]

Millennium Management: Secure machine learning using Amazon SageMaker

This is a guest post from Millennium Management. In their own words, “Millennium Management is a global investment management firm, established in 1989, with over 2,900 employees and $39.2 billion in assets under management as of August 2, 2019.” Millennium Management is comprised of a large number of specialized trading teams across the United States, […]

Amazon Comprehend now supports multi-label custom classification

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Amazon Comprehend supports custom classification and enables you to build custom classifiers that are specific to your requirements, without the need for any ML expertise. Previously, custom classification supported multi-class classification, which is […]

Building a business intelligence dashboard for your Amazon Lex bots

You’ve rolled out a conversational interface powered by Amazon Lex, with a goal of improving the user experience for your customers. Now you want to track how well it’s working. Are your customers finding it helpful? How are they using it? Do they like it enough to come back? How can you analyze their interactions […]

Maximizing NLP model performance with automatic model tuning in Amazon SageMaker

The field of Natural Language Processing (NLP) has had many remarkable breakthroughs in the past two years. Advanced deep learning models are raising the state-of-the-art performance standards for NLP tasks. To benefit from newly published NLP models, the best approach is to apply a pre-trained language model to a new dataset and fine-tune it for […]

NeurIPS competition tackles climate data challenges

The Earth’s climate is a highly complex, dynamic system. It is difficult to understand and predict how different climate variables interact. Finding causal relations in climate research today relies mostly on expensive and time-consuming model simulations. Fortunately, with the explosion in the availability of large-scale climate data and increasing computational power via the cloud, there […]

Interpreting 3D seismic data automatically using Amazon SageMaker

Interpreting 3D seismic data correctly helps identify geological features that may hold or trap oil and gas deposits. Amazon SageMaker and Apache MXNet on AWS can automate horizon picking using deep learning techniques. In this post, I use these services to build and train a custom deep-learning model for the interpretation of geological features on […]

Standard Voices in Amazon Polly now available in Middle East and Asia Pacific Regions

Amazon Polly turns text into lifelike speech, which allows you to create voice-enabled applications. AWS is excited to announce the general availability of all standard voices in the Middle East (Bahrain) and Asia Pacific (Hong Kong) Regions. Customers in these Regions can now synthesize over 60 standard voices available in 29 languages in the Amazon […]

Cinnamon AI saves 70% on ML model training costs with Amazon SageMaker Managed Spot Training

Developers are constantly training and re-training machine learning (ML) models so they can continuously improve model predictions. Depending on the dataset size, model training jobs can take anywhere from a few minutes to multiple hours or days. ML development can be a complex, expensive, and iterative process. Being compute intensive, keeping compute costs low for […]

Building machine learning workflows with AWS Data Exchange and Amazon SageMaker

Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant inspection grades and scores from AWS […]