Category: Amazon Machine Learning

It is fourth-and-one on the Texans’ 36-yard line with 3:21 remaining on the clock in a tie game. Should the Colts’ head coach Frank Reich send out kicker Rodrigo Blankenship to attempt a 54-yard field goal or rely on his offense to convert a first down? Frank chose to go for it, leading to a […]

Amazon SageMaker Ground Truth supports many different types of labeling jobs, including several image-based labeling workflows like image-level labels, bounding box-specific labels, or pixel-level labeling. For situations not covered by these standard approaches, Ground Truth also supports custom image-based labeling, which allows you to create a labeling workflow with a completely unique UI and associated […]

Patient readmission to hospital after prior visits for the same disease results in an additional burden on healthcare providers, the health system, and patients. Machine learning (ML) models, if built and trained properly, can help understand reasons for readmission, and predict readmission accurately. ML could allow providers to create better treatment plans and care, which […]

AWS recently released the new Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like databases or message queues simply by using the Kubernetes API. […]

With the growing adoption of Machine Learning (ML) across industries, there is an increasing demand for faster and easier ways to run ML inference at scale. ML use cases, such as manufacturing defect detection, demand forecasting, fraud surveillance, and many others, involve tens or thousands of datasets, including images, videos, files, documents, and other artifacts. […]

As artificial intelligence (AI) and machine learning (ML) technologies continue to proliferate, using ML models plays a crucial role in converting the insights from data into actual business impacts. Operational ML means streamlining every step of the ML lifecycle and deploying the best models within the existing production system. And within that production system, the […]

July 2023: Please refer to the Media Replay Engine (MRE) solution presented in this Github repo instead, for the latest and more efficient solution for this use case. MRE is a framework for building automated video clipping and replay (highlight) generation pipelines using AWS services for live and video-on-demand (VOD) content. Extracting highlights from a […]

Note to readers: Enrollment for this course has been temporarily paused until the start of 2022. Stay tuned for further announcements.  AWS and NVIDIA are excited to announce the new Hands-on Machine Learning with Amazon SageMaker and NVIDIA GPUs course. The course has four parts, and is designed to help machine learning (ML) enthusiasts quickly learn […]

Whether you are developing a machine learning (ML) model for reducing operating cost, improving efficiency, or improving customer satisfaction, there are no perfect solutions when it comes to producing an effective model. From an ML development perspective, data scientists typically go through stages of data exploration, feature engineering, model development, and model training and tuning […]

Eastern Australia is among the most fire-prone regions in the world. Although bushfires are a regular occurrence in Australia, the 2019–2020 bushfire crisis set ablaze over 17 million hectares of land (larger than the size of England), costing the Australian economy more than $100 billion between property, infrastructure, social, and environmental costs. With increasingly extreme […]