Category: Amazon SageMaker

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, […]

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 […]

Spectral MD, Inc. is a clinical research stage medical device company that describes itself as “breaking the barriers of light to see deep inside the body.” Recently designated by the FDA as a “Breakthrough Device,” Spectral MD provides an impressive solution to wound care using cutting edge multispectral imaging and deep learning technologies. This Dallas-based […]

The Amazon ML Solutions Lab worked with Kinect Energy recently to build a pipeline to predict future energy prices based on machine learning (ML). We created an automated data ingestion and inference pipeline using Amazon SageMaker and AWS Step Functions to automate and schedule energy price prediction. The process makes special use of the Amazon […]

Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth enables you to efficiently and accurately label the datasets required to train machine learning (ML) systems. Ground Truth provides built-in labeling workflows that take human labelers step-by-step through tasks and provide tools to help them produce good results. Built-in workflows are currently available for object detection, […]

By the year 2050, our planet will need to feed ten billion people. We can’t expand the earth to create more agricultural land, so the solution to growing more food is to make agriculture more productive and less resource-dependent. In other words, there is no room for crop losses or resource waste. Bayer is using […]

Git integration is now available in the Amazon SageMaker Python SDK. You no longer have to download scripts from a Git repository for training jobs and hosting models. With this new feature, you can use training scripts stored in Git repos directly when training a model in the Python SDK. You can also use hosting […]

With a few clicks in the Amazon SageMaker console or a few one-line API calls, you can now quickly search, filter, and sort your machine learning (ML) experiments using key model attributes, such as hyperparameter values and accuracy metrics, to help you more quickly identify the best models for your use case and get to […]

This is a guest blog post by Dante Monaldo, co-founder and CTO of Pluto Money Pluto Money, a San Francisco-based startup, is a free money management app that combines banking, behavioral economics, and machine learning (ML) to guide Generation Z towards their financial goals in college and beyond. We’re building the first mobile bank designed […]

April 2023: Please refer to the fastai course material for updated content Deep learning is changing the world. However, much of the foundation work, such as building containers, can slow you down. This post describes how you can build, train, and deploy fastai models into Amazon SageMaker training and hosting by using the Amazon SageMaker […]