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 […]
Amazon Sagemaker
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Back to news homeUse 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 […]
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 […]
Train fraudulent payment detection with Amazon SageMaker
The ability to detect fraudulent card payments is becoming increasingly important as the world moves towards a cashless society. For decades, banks have relied on building complex mathematical models to […]
Perform interactive data engineering and data science workflows from Amazon SageMaker Studio notebooks
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Studio notebooks to […]
Launch Amazon SageMaker Studio from external applications using presigned URLs
Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10 times. Studio gives you […]
Get value from every customer touchpoint using Amazon Connect as a data gathering mechanism
The recent pandemic and the impossibility of meeting customers in person has made two-way contact centers an effective tool for sales representatives. Amazon Connect is the ideal service to manage […]
Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language
You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), […]
Build machine learning at the edge applications using Amazon SageMaker Edge Manager and AWS IoT Greengrass V2
Running machine learning (ML) models at the edge can be a powerful enhancement for Internet of Things (IoT) solutions that must perform inference without a constant connection back to the […]
Schedule an Amazon SageMaker Data Wrangler flow to process new data periodically using AWS Lambda functions
Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple […]