AWS Machine Learning Blog

Category: Artificial Intelligence

Detect fraudulent transactions using machine learning with Amazon SageMaker

Businesses can lose billions of dollars each year due to malicious users and fraudulent transactions. As more and more business operations move online, fraud and abuses in online systems are also on the rise. To combat online fraud, many businesses have been using rule-based fraud detection systems. However, traditional fraud detection systems rely on a […]

Implement RStudio on your AWS environment and access your data lake using AWS Lake Formation permissions

R is a popular analytic programming language used by data scientists and analysts to perform data processing, conduct statistical analyses, create data visualizations, and build machine learning (ML) models. RStudio, the integrated development environment for R, provides open-source tools and enterprise-ready professional software for teams to develop and share their work across their organization building, […]

Model hosting patterns in Amazon SageMaker, Part 4: Design patterns for serial inference on Amazon SageMaker

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models. These models can be sequentially combined to perform various tasks, such as preprocessing, data transformation, model selection, inference […]

Train a time series forecasting model faster with Amazon SageMaker Canvas Quick build

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how […]

Use Amazon SageMaker Canvas for exploratory data analysis

Exploratory data analysis (EDA) is a common task performed by business analysts to discover patterns, understand relationships, validate assumptions, and identify anomalies in their data. In machine learning (ML), it’s important to first understand the data and its relationships before getting into model building. Traditional ML development cycles can sometimes take months and require advanced […]

Model hosting patterns in Amazon SageMaker, Part 7: Run ensemble ML models on Amazon SageMaker

Model deployment in machine learning (ML) is becoming increasingly complex. You want to deploy not just one ML model but large groups of ML models represented as ensemble workflows. These workflows are comprised of multiple ML models. Productionizing these ML models is challenging because you need to adhere to various performance and latency requirements. Amazon […]

Host code-server on Amazon SageMaker

Machine learning (ML) teams need the flexibility to choose their integrated development environment (IDE) when working on a project. It allows you to have a productive developer experience and innovate at speed. You may even use multiple IDEs within a project. Amazon SageMaker lets ML teams choose to work from fully managed, cloud-based environments within […]

Real estate brokerage firm John L. Scott uses Amazon Textract and Amazon Comprehend to strike racially restrictive language from property deeds for homeowners

Founded more than 91 years ago in Seattle, John L. Scott Real Estate’s core value is Living Life as a Contribution®. The firm helps homebuyers find and buy the home of their dreams, while also helping sellers move into the next chapter of their home ownership journey. John L. Scott currently operates over 100 offices […]

Model hosting patterns in Amazon SageMaker, Part 3: Run and optimize multi-model inference with Amazon SageMaker multi-model endpoints

Amazon SageMaker multi-model endpoint (MME) enables you to cost-effectively deploy and host multiple models in a single endpoint and then horizontally scale the endpoint to achieve scale. As illustrated in the following figure, this is an effective technique to implement multi-tenancy of models within your machine learning (ML) infrastructure. We have seen software as a […]

Testing approaches for Amazon SageMaker ML models

This post was co-written with Tobias Wenzel, Software Engineering Manager for the Intuit Machine Learning Platform. We all appreciate the importance of a high-quality and reliable machine learning (ML) model when using autonomous driving or interacting with Alexa, for examples. ML models also play an important role in less obvious ways—they’re used by business applications, […]