AWS Machine Learning Blog

Building a deep neural net–based surrogate function for global optimization using PyTorch on Amazon SageMaker

July 2023: This post was reviewed for accuracy. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. Customer X has the following problem: They are about to release a new car model to be designed for maximum fuel efficiency. In reality, thousands of […]

Launching TensorFlow distributed training easily with Horovod or Parameter Servers in Amazon SageMaker

Amazon SageMaker supports all the popular deep learning frameworks, including TensorFlow. Over 85% of TensorFlow projects in the cloud run on AWS. Many of these projects already run in Amazon SageMaker. This is due to the many conveniences Amazon SageMaker provides for TensorFlow model hosting and training, including fully managed distributed training with Horovod and […]

Performing batch inference with TensorFlow Serving in Amazon SageMaker

After you’ve trained and exported a TensorFlow model, you can use Amazon SageMaker to perform inferences using your model. You can either: Deploy your model to an endpoint to obtain real-time inferences from your model. Use batch transform to obtain inferences on an entire dataset stored in Amazon S3. In the case of batch transform, […]

Optimizing TensorFlow model serving with Kubernetes and Amazon Elastic Inference

This post offers a dive deep into how to use Amazon Elastic Inference with Amazon Elastic Kubernetes Service. When you combine Elastic Inference with EKS, you can run low-cost, scalable inference workloads with your preferred container orchestration system. Elastic Inference is an increasingly popular way to run low-cost inference workloads on AWS. It allows you […]

Tracking the throughput of your private labeling team through Amazon SageMaker Ground Truth

Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for your machine learning models. 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 Truth can lower your […]

Enable smart text analytics using Amazon OpenSearch Service and Amazon Comprehend

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. We’re excited to announce an end-to-end solution that leverages natural language processing to analyze and visualize unstructured text in your Amazon OpenSearch Service domain with Amazon Comprehend in the AWS Cloud. You can deploy this solution in minutes with an […]

Build a custom entity recognizer using Amazon Comprehend

Amazon Comprehend is a natural language processing service that can extract key phrases, places, names, organizations, events, and even sentiment from unstructured text, and more. Customers usually want to add their own entity types unique to their business, like proprietary part codes or industry-specific terms. In November 2018, enhancements to Amazon Comprehend added the ability to […]

Power contextual bandits using continual learning with Amazon SageMaker RL

Amazon SageMaker is a modular, fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Training models is quick and easy using a set of built-in high-performance algorithms, pre-built deep learning frameworks, or using your own framework. To help select your machine learning (ML) algorithm, […]

Speed up training on Amazon SageMaker using Amazon FSx for Lustre and Amazon EFS file systems

April 2021 – The Amazon FSx section of this post has been updated to cover changes introduced to mount point names with scratch_2 and persistent_1 deployment options. Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the most important capabilities of Amazon SageMaker is its ability to run fully […]

Serving deep learning at Curalate with Apache MXNet, AWS Lambda, and Amazon Elastic Inference

This is a guest blog post by Jesse Brizzi, a computer vision research engineer at Curalate. At Curalate, we’re always coming up with new ways to use deep learning and computer vision to find and leverage user-generated content (UGC) and activate influencers. Some of these applications, like Intelligent Product Tagging, require deep learning models to […]