Artificial Intelligence
Category: Amazon SageMaker
Performing anomaly detection on industrial equipment using audio signals
Industrial companies have been collecting a massive amount of time-series data about operating processes, manufacturing production lines, and industrial equipment. You might store years of data in historian systems or in your factory information system at large. Whether you’re looking to prevent equipment breakdown that would stop a production line, avoid catastrophic failures in a […]
Managing your machine learning lifecycle with MLflow and Amazon SageMaker
June 2024: The contents of this post are out of date. We recommend you refer to Announcing the general availability of fully managed MLflow on Amazon SageMaker for the latest. With the rapid adoption of machine learning (ML) and MLOps, enterprises want to increase the velocity of ML projects from experimentation to production. During the […]
Understanding the key capabilities of Amazon SageMaker Feature Store
October 2022: This post was reviewed and updated for accuracy. One of the challenging parts of machine learning (ML) is feature engineering, the process of transforming data to create features for ML. Features are processed data signals used for training ML models and for deployed models to make accurate predictions. Data scientists and ML engineers […]
Saving time with personalized videos using AWS machine learning
CLIPr aspires to help save 1 billion hours of people’s time. We organize video into a first-class, searchable data source that unlocks the content most relevant to your interests using AWS machine learning (ML) services. CLIPr simplifies the extraction of information in videos, saving you hours by eliminating the need to skim through them manually […]
Building your own brand detection and visibility using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels – Part 1: End-to-end solution
According to Gartner, 58% of marketing leaders believe brand is a critical driver of buyer behavior for prospects, and 65% believe it’s a critical driver of buyer behavior for existing customers. Companies spend huge amounts of money on advertisement to raise brand visibility and awareness. In fact, as per Gartner, CMO spends over 21% of […]
Multi-account model deployment with Amazon SageMaker Pipelines
Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps). Creating multiple accounts to organize all the resources of your organization is a good DevOps practice. A multi-account […]
Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines
March 2025: This post was reviewed and updated for accuracy. We have Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your […]
Labeling mixed-source, industrial datasets with Amazon SageMaker Ground Truth
Prior to using any kind of supervised machine learning (ML) algorithm, data has to be labeled. Amazon SageMaker Ground Truth simplifies and accelerates this task. Ground Truth uses pre-defined templates to assign labels that classify the content of images or videos or verify existing labels. Ground Truth allows you to define workflows for labeling various […]
Building predictive disease models using Amazon SageMaker with Amazon HealthLake normalized data
In this post, we walk you through the steps to build machine learning (ML) models in Amazon SageMaker with data stored in Amazon HealthLake using two example predictive disease models we trained on sample data using the MIMIC-III dataset. This dataset was developed by the MIT lab for Computational Physiology and consists of de-identified healthcare […]
Using machine learning to predict vessel time of arrival with Amazon SageMaker
According to the International Chamber of Shipping, 90% of world commerce happens at sea. Vessels are transporting every possible kind of commodity, including raw materials and semi-finished and finished goods, making ocean transportation a key component of the global supply chain. Manufacturers, retailers, and the end consumer are reliant on hundreds of thousands of ships […]