Category: Amazon SageMaker

Given the rising demand for fast and effective COVID-19 detection, customers are exploring the usage of respiratory sound data, like coughing, breathing, and counting, to automatically diagnose COVID-19 based on machine learning (ML) models. University of Cambridge researchers built a COVID-19 sound application and demonstrated that a simple binary ML classifier can classify healthy and […]

As businesses around the world are embarking on building innovative solutions, we’re seeing a growing trend adopting data science workloads across various industries. Recently, we’ve seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to […]

July 2023: This post was reviewed for accuracy. In this post, we discuss solving numerical optimization problems using the very flexible Amazon SageMaker Processing API. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. This pattern is relevant to solving business-critical problems such […]

This blog post was reviewed and updated June, 2022 to include new features that have been added to the Data processing such as Amazon SageMaker Studio and EMR integration. Training an accurate machine learning (ML) model requires many different steps, but none are potentially more important than data processing. Examples of processing steps include converting […]

As businesses and IT leaders look to accelerate the adoption of machine learning (ML) and artificial intelligence (AI), there is a growing need to understand how to build secure and compliant ML environments that meet enterprise requirements. One major challenge you may face is integrating ML workflows into existing IT and business work streams. A […]

The ability to rapidly iterate and train machine learning (ML) models is key to deriving business value from ML workloads. Because ML models often have many tunable parameters (known as hyperparameters) that can influence the model’s ability to effectively learn, data scientists often use a technique known as hyperparameter optimization (HPO) to achieve the best-performing […]

With the rapid growth of object detection techniques, several frameworks with packaged pre-trained models have been developed to provide users easy access to transfer learning. For example, GluonCV, Detectron2, and the TensorFlow Object Detection API are three popular computer vision frameworks with pre-trained models. In this post, we use Amazon SageMaker to build, train, and […]

Machine learning (ML)-based solutions are capable of solving complex problems, from voice recognition to finding and identifying faces in video clips or photographs. Usually, these solutions use large amounts of training data, which results in a model that processes input data and produces numeric output that can be interpreted as a word, face, or classification […]

Amazon SageMaker Debugger can monitor ML model parameters, metrics, and computation resources as the model optimization is in progress. You can use it to identify issues during training, gain insights, and take actions like stopping the training or sending notifications through built-in or custom actions. Debugger is particularly useful in training challenging deep learning model […]

Hello and welcome to our first “This month in AWS Machine Learning” of 2021! Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each […]