AWS Continues AI Push in Cloud Services
The Amazon Web Services Inc. (AWS) cloud was quick to leverage machine learning, deep learning and other artificial intelligence technologies to improve its cloud services and has continued its AI push, with a new telecommunication service as the latest example.
AWS yesterday (Nov. 6) introduced Machine Learning for Telecommunication, described as "a framework for an end-to-end machine learning (ML) process including ad-hoc data exploration, data processing and feature engineering, and model training and evaluation."
It targets the telecommunication sector with a customized data set that demonstrates the use of ML algorithms for testing and training models for predictive analysis in telecommunication.
The service uses the company's fully managed ML service, Amazon SageMaker and the open source Web application, Jupyter Notebook, which helps developers create and share live code, equations, visualizations and narrative text.
"Customers can use the included notebooks as a starting point to develop their own custom ML models, and customize the included Jupyter notebooks for their own use case," AWS said in an announcement.
Just within the last week, AWS made these other announcements that further demonstrate its AI push:
- Chainer 5.0 on AWS Deep Learning AMIs. AWS on Nov. 5 announced: "The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with Chainer 5.0 , which includes support for Python 3.6 and iDeep 2.0. As with other frameworks, Deep Learning AMIs offer optimized builds of Chainer 5.0 that are fine-tuned and fully-configured for high performance deep learning on Amazon EC2 CPU and GPU instances."
- Amazon SageMaker support for Pipe Mode for datasets in CSV format. AWS on Nov. 5 said: "The built-in algorithms that come with Amazon SageMaker now support Pipe Mode for datasets in CSV format. This accelerates the speed at which data can be streamed from Amazon Simple Storage Service (S3) into SageMaker by up to 40 percent, while training machine learning (ML) models. With this new enhancement, the performance benefits of Pipe Mode are extended to training datasets in CSV format in addition to the protobuf recordIO format that we released earlier this year."
- More accurate object and scene detection in Amazon Rekognition. AWS announced on Nov. 2 that its deep learning-based analysis service used to identify objects, people, text, scenes and activities in images and scenes can now more accurately detect objects and scenes (called "label detection") and locate objects in images. "Label detection identifies objects and scenes in images," AWS said. "Until now, Amazon Rekognition could identify the presence of an object in an image, but couldn't find where the object is within the image. Amazon Rekognition can now specify the location of common objects such as dogs, people and cars in an image by returning object bounding boxes, and comes with significantly improved accuracy for all existing object and scene labels across a variety of use cases."
More information on Amazon Rekognition and other AWS AI services such as Amazon Machine Learning can be found on the Artificial Intelligence site, the dedicated Machine Learning on AWS site and the Machine Learning & Artificial Intelligence site, which details AWS Marketplace offerings.
David Ramel is the editor of Visual Studio Magazine.