Category: Customer Enablement

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 […]

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 […]

AWS DeepRacer is a fun and easy way for developers with no prior experience to get started with machine learning (ML). At the end of the 2019 season, the AWS DeepRacer League engaged the Amazon ML Solutions Lab to develop a new sports analytics feature for the AWS DeepRacer Championship Cup at re:Invent 2019. The […]

The recent post Working from Home? Here’s How AWS Can Help shared several ways AWS is helping you set up and scale remote work and work-from-home initiatives. Getting these solutions set up is sometimes best—and achieved more quickly—with expert help. You can get the help you need with AWS IQ, which connects you to AWS […]

AWS introduced the Machine Learning (ML) Solutions Lab a little over two years ago to connect our machine learning experts and data scientists with AWS customers.  Our goal was to help our customers solve their most pressing business problems using ML. We’ve helped our customers increase fraud detection rates, improved forecasting and predictions for more […]

This post looks at the role machine learning plays in providing fans with deeper insights into the game. We also provide code snippets that show the training and deployment process behind these insights on Amazon SageMaker.

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 […]

This post was reviewed and updated May 2022, to enforce model results reproducibility, add reproducibility checks, and to add a batch transform example for model predictions. Previously, this post was updated March 2021 to include SageMaker Neo compilation. Updated the compatibility for model trained using Keras 2.2.x with h5py 2.10.0 and TensorFlow 1.15.3. Amazon SageMaker […]

Amazon SageMaker seq2seq offers you a very simple way to make use of the state-of-the-art encoder-decoder architecture (including the attention mechanism) for your sequence to sequence tasks. You just need to prepare your sequence data in recordio-protobuf format and your vocabulary mapping files in JSON format. Then you need to upload them to Amazon Simple […]

We are excited to announce the Amazon ML Solutions Lab, a new program that connects machine learning experts from across Amazon with AWS customers to help identify novel uses of machine learning inside customers’ businesses, and guide them in developing new machine learning-enabled features, products, and processes. Amazon has been investing in machine learning for more than 20 years, innovating in areas such as fulfilment and logistics, personalization and recommendations, forecasting, fraud prevention, and supply chain optimization. The Amazon ML Solutions Lab provides you access to the same talent that built many of Amazon’s machine learning-powered products and services.