Artificial Intelligence
Category: Amazon SageMaker
Build a system for catching adverse events in real-time using Amazon SageMaker and Amazon QuickSight
Social media platforms provide a channel of communication for consumers to talk about various products, including the medications they take. For pharmaceutical companies, monitoring and effectively tracking product performance provides customer feedback about the product, which is vital to maintaining and improving patient safety. However, when an unexpected medical occurrence resulting from a pharmaceutical product […]
Create Amazon SageMaker projects with image building CI/CD pipelines
Amazon SageMaker projects are AWS Service Catalog provisioned products that enable you to easily create end-to-end machine learning (ML) solutions. SageMaker projects give organizations the ability to use templates that bootstrap ML solutions for your users to speed up the start time for ML development. You can now use SageMaker projects to manage custom dependencies […]
Use pre-trained financial language models for transfer learning in Amazon SageMaker JumpStart
Starting today, we’re releasing new tools for multimodal financial analysis within Amazon SageMaker JumpStart. SageMaker JumpStart helps you quickly and easily get started with machine learning (ML). It provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few clicks. You can now access […]
Use SEC text for ratings classification using multimodal ML in Amazon SageMaker JumpStart
Starting today, we’re releasing new tools for multimodal financial analysis within Amazon SageMaker JumpStart. SageMaker JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few clicks. You can now access […]
Virtu Financial enables its customers to apply advanced analytics and machine learning on trade and market data by provisioning Amazon SageMaker
This is a guest post by Erin Stanton, who currently runs the Global Client Support organization for Virtu Analytics. Virtu Financial is a leading provider of financial services and products that uses cutting-edge technology to deliver liquidity to the global markets and innovative, transparent trading solutions to its clients. Virtu uses its global market-making expertise […]
Ounass increases its revenue using Amazon SageMaker with a Word2vec based recommender system
Based in Dubai, Ounass is the Middle East’s leading ecommerce platform for luxury goods. Scouring the globe for leading trends, Ounass’s expert team reports on the latest fashion updates, coveted insider information, and exclusive interviews for customers to read and shop. With more than 230,000 unique catalog items spanning multiple brands and several product classes—including […]
Customize Amazon SageMaker Studio using Lifecycle Configurations
July 2023: This post was reviewed for accuracy. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. It provides all the tools you need to take your models from experimentation to production while boosting your productivity. You can […]
Model and data lineage in machine learning experimentation
Modern quantitative finance is based around the approach of pattern recognition in historical data. This approach requires teams of scientists to work in a collaborative and regulated setting in order to develop models that can be used to make trading predictions. With the growing influence of this field, both participants and regulators are looking to […]
Use the AWS Cloud for observational life sciences studies
In this post, we discuss how to use the AWS Cloud and its services to accelerate observational studies for life sciences customers. We provide a reference architecture for architects, business owners, and technology decision-makers in the life sciences industry to automate the processes in clinical studies. Observational studies lead the way in research, allowing you […]
Scale ML feature ingestion using Amazon SageMaker Feature Store
Amazon SageMaker Feature Store is a purpose-built solution for machine learning (ML) feature management. It helps data science teams reuse ML features across teams and models, serves features for model predictions at scale with low latency, and train and deploy new models more quickly and effectively. As you learn about how to use a feature […]