AWS Machine Learning Blog

Category: Technical How-to

Create synthetic data for computer vision pipelines on AWS

Collecting and annotating image data is one of the most resource-intensive tasks on any computer vision project. It can take months at a time to fully collect, analyze, and experiment with image streams at the level you need in order to compete in the current marketplace. Even after you’ve successfully collected data, you still have […]

To better illustrate the changes, the following figure displays both a standard MLOps pipeline created automatically by SageMaker (Steps 1-5) as well as changes required to extend it to a secondary Region (Steps 6-11).

Enable CI/CD of multi-Region Amazon SageMaker endpoints

Amazon SageMaker and SageMaker inference endpoints provide a capability of training and deploying your AI and machine learning (ML) workloads. With inference endpoints, you can deploy your models for real-time or batch inference. The endpoints support various types of ML models hosted using AWS Deep Learning Containers or your own containers with custom AI/ML algorithms. […]

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 […]

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 […]

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 […]

Customize business rules for intelligent document processing with human review and BI visualization

A massive amount of business documents are processed daily across industries. Many of these documents are paper-based, scanned into your system as images, or in an unstructured format like PDF. Each company may apply unique rules associated with its business background while processing these documents. How to extract information accurately and process them flexibly is […]

Automate classification of IT service requests with an Amazon Comprehend custom classifier

Enterprises often deal with large volumes of IT service requests. Traditionally, the burden is put on the requester to choose the correct category for every issue. A manual error or misclassification of a ticket usually means a delay in resolving the IT service request. This can result in reduced productivity, a decrease in customer satisfaction, […]

Reduce cost and development time with Amazon SageMaker Pipelines local mode

Creating robust and reusable machine learning (ML) pipelines can be a complex and time-consuming process. Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Creating and running a full pipeline during experimentation adds unwanted overhead and cost to the development lifecycle. In this post, we […]

Automate your time series forecasting in Snowflake using Amazon Forecast

This post is a joint collaboration with Andries Engelbrecht and James Sun of Snowflake, Inc. The cloud computing revolution has enabled businesses to capture and retain corporate and organizational data without capacity planning or data retention constraints. Now, with diverse and vast reserves of longitudinal data, companies are increasingly able to find novel and impactful […]