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AWS announces 13 new machine learning services and capabilities

01 December 2018
AWS announces 13 new machine learning services and capabilities

The cloud environment is built on Amazon SageMaker, a service that was launched a year ago and created to make it cheaper for everyday developers to build, train and deploy machine learning "into a production-ready hosted environment".'s cloud services platform, has introduced two new services based on blockchain technology.

AWS revealed that the customers will be abel to use the Inferentia with the TensorFlow AI software, which is created by the Google, as well as some of the other AI frameworks like the ONNX and PyTorch format for converting models.

"AWS's extensive functionality is critical to NAB's transformation, giving us far more flexibility, empowering us to move much faster, and allowing us to rapidly scale as we expand our use of cloud technologies", said Yuri Misnik, Executive General Manager, Business Enabling Technology at National Australia Bank.

How the machine learning service will fit in with Amazon's other healthcare endeavors remains to be seen, but there is little question that the company is gunning for a level of insight into personal health that will place it head and shoulders above any other competitors. Racers can also compete in virtual events and tournaments throughout the year by entering time trials on special tracks in the AWS DeepRacer simulator, available in the AWS Management Console. "We're excited to expand our relationship with NAB as their strategic cloud provider as they migrate more workloads to AWS and help them realize efficiencies and benefits in areas like performance computing, security and compliance, data analytics, and machine learning". SageMaker Neo supports hardware platforms from NVIDIA, Intel, Xilinx, Cadence, and Arm, and popular frameworks such as TensorFlow, Apache MXNet, and PyTorch.

The cloud software combines text analysis and machine learning to read patient records that often consist of prescriptions, notes, audio interviews, and test reports. Comprehend Medical requires no machine learning expertise, no complicated rules to write, no models to train, and it is continuously improving. Experience has shown that there is no master algorithm for personalisation. The fully autonomous race auto uses reinforcement learning models from SageMaker and allows the developers to race against each other for prizes in their own DeepRacer League.

"The Amazon Managed Blockchain is going to eliminate the overhead cost required for the development of a new network". Amazon Personalize can make recommendations, personalise search results, and segment customers for direct and personalised marketing through email or push notifications. It is not a blockchain platform, but to use in conjunction with Amazon's blockchain product to "maintain a complete and verifiable history of data changes".

Kurt Marko, an independent technology analyst told Computerworld UK: "Symbolically, Outposts is another acknowledgement by AWS that most enterprises want or need to split workloads and data between on-premise systems and public cloud services".