Category: Amazon SageMaker

The machine learning (ML) model-building process requires data scientists to manually prepare data features, select an appropriate algorithm, and optimize its model parameters. It involves a lot of effort and expertise. Amazon SageMaker Autopilot removes the heavy lifting required by this ML process. It inspects your dataset, generates several ML pipelines, and compares their performance […]

There are many techniques to train deep learning models with a small amount of data. Examples include transfer learning, few-shot learning, or even one-shot learning for an image classification task and fine-tuning for language models based on a pre-trained BERT or GPT2 model. However, you may still have a use case in which you need […]

Machine learning (ML) workflows orchestrate and automate sequences of ML tasks, including data collection, training, testing, evaluating an ML model, and deploying the models for inference. AWS Step Functions automates and orchestrates Amazon SageMaker-related tasks in an end-to-end workflow. The AWS Step Functions Data Science Software Development Kit (SDK) is an open-source library that allows […]

One of AWS’s goals is to put machine learning (ML) in the hands of every developer. With the open-source AutoML library AutoGluon, deployed using Amazon SageMaker and AWS Lambda, we can take this a step further, putting ML in the hands of anyone who wants to make predictions based on data—no prior programming or data […]

This blog post is co-written by guest authors from SNCF and Olexya. Transportation and logistics are fertile ground for machine learning (ML). In this post, we show how the French state-owned railway company Société Nationale des Chemins de fer Français (SNCF) uses ML from AWS with the help of its technology partner Olexya to research, […]

Update Aug 12, 2020 – New features: Amazon Comprehend adds five new languages(Spanish, French, German, Italian and Portuguese) read here. Amazon Comprehend increased the limit of number of entities per custom entity model from 12 to 25 read here. Organizations across industries have a lot of unstructured data that you can evaluate to get entity-based […]

When you build models with the Apache MXNet deep learning framework, you can take advantage of the expansive model zoo provided by GluonCV to quickly train state-of-the-art computer vision algorithms for image and video processing. A typical development environment for training consists of a Jupyter notebook hosted on a compute instance configured by the operating […]

This post is co-written by Femi Olumofin, an analytics architect at Infoblox. In the same way that you can conveniently recognize someone by name instead of government-issued ID or telephone number, the Domain Name System (DNS) provides a convenient means for naming and reaching internet services or resources behind IP addresses. The pervasiveness of DNS, […]

November 2022: The solution described here is not the latest best practice. The new HuggingFace Deep Learning Container (DLC) is available in Amazon SageMaker (see Use Hugging Face with Amazon SageMaker). For customer training BERT models, the recommended pattern is to use HuggingFace DLC, shown as in Finetuning Hugging Face DistilBERT with Amazon Reviews Polarity dataset. […]

Convolutional neural networks (CNNs) achieve state-of-the-art results in tasks such as image classification and object detection. They are used in many diverse applications, such as in autonomous driving to detect traffic signs and objects on the street, in healthcare to more accurately classify anomalies in image-based data, and in retail for inventory management. However, CNNs […]