Category: SageMaker

Back in November 2018 we announced on this blog that the same machine learning (ML) courses used to train engineers at Amazon are now available to all developers through AWS. Today, we’re letting you know that there is a way to enhance and validate your ability to build, train, tune, and deploy machine learning models […]

In this blog post, we’ll discuss how to implement custom, state-of-the-art hyperparameter optimization (HPO) algorithms to tune models on Amazon SageMaker. Amazon SageMaker includes a built-in HPO algorithm, but provides the flexibility to use your own HPO algorithm. We’ll provide you with a framework to incorporate an HPO algorithm that you choose. However, before we […]

In this blog post we’re going to walk you through an example situation where you’ve just built a machine learning system that labels your data at volume and you want to perform manual quality assurance (QA) on some of the labels. How can you do so without overwhelming your limited resources?  We’ll show you how, […]

We’re excited to announce the availability of two more data labeling services that you can use for your Amazon SageMaker Ground Truth labeling jobs: Data Labeling Services by iMerit’s US-based workforce Data Labeling Services by Startek, Inc. These new listings on the AWS Marketplace supplement the existing iMerit India-based workforce listing to provide you a […]

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for […]

Custom object detection has become an important enabler for a wide range of industries and use cases—such as finding tumors in MRIs, identifying diseased crops, and monitoring railway platforms. In this blog post, we build a bird identifier based on an annotated public dataset. This type of model could be used in a number of […]

At re:Invent 2018 we launched Amazon SageMaker Ground Truth, which can Build Highly Accurate Datasets and Reduce Labeling Costs by up to 70% using machine learning. Amazon SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, Amazon SageMaker Ground […]

In this blog post, I’ll discuss how to use Amazon SageMaker script mode to train models with TensorFlow’s eager execution mode. Eager execution is the future of TensorFlow; although it is available now as an option in recent versions of TensorFlow 1.x, it will become the default mode of TensorFlow 2. I’ll provide a brief […]

With Amazon SageMaker Ground Truth, you can easily and inexpensively build more accurately labeled machine learning datasets. To decrease labeling costs, use Ground Truth machine learning to choose “difficult” images that require human annotation and “easy” images that can be automatically labeled with machine learning. This post explains how automated data labeling works and how […]

DXC Technology is a global IT service leader providing end-to-end services on Digital Transformation to businesses and governments. They also provide service management to their clients on-premises and in the cloud.  The incident tickets raised as part of the process need to be resolved quickly to meet their service level agreements (SLA).  DXC has  goals […]