Amazon Web Services (AWS) has taken square aim at the data centre by tying in VMware technology and rolling out two new services and on-premise hardware to help customers build and support hybrid clouds. "Amazon SageMaker RL, the pre-built models, and available frameworks made everything really accessible, such that in less than a day I was able to have this fantastic outcome".
"VMware Cloud on AWS broke the barriers between the data center and the cloud by combining the best of the private cloud and public cloud in the AWS cloud", said Pat Gelsinger, chief executive officer, VMware.
"There just isn't a lot of value in that type of on-premises offering and that's why these solutions aren't getting much traction", he added.
Drawing on its chip-development group, AWS also said that it has built custom chips for artificial intelligence that will be available from its cloud, following the lead of other internet companies like Google and Alibaba.
On the Amazon Quantum Ledger Database or QLDB, customers can replicate a copy of their blockchain network activity. The cloud environment is built on Amazon SageMaker, a service that was launched past year and created to make it cheaper for everyday developers to build, train and deploy machine learning "into a production-ready hosted environment".
Amazon Comprehend Medical uses natural language processing to simplify the process of using machine learning to extract pertinent information from unstructured medical text, such as medical notes, prescriptions, interview transcripts, and pathology and radiology reports. With a few clicks in the AWS Management Console, customers can set up a blockchain network that can span multiple AWS accounts and scale to support thousands of applications and millions of transactions. Using AWS Ground Station, customers can save up to 80 percent of their ground station costs by paying for antenna access time on demand, and they can rely on AWS Ground Station's global footprint of ground stations to downlink data when and where they need it. Customers don't need experience in machine learning, either. Users can train reinforcement learning models in an online simulator and then test-drive them on DeepRacer. Experience has shown that there is no master algorithm for personalisation.
This is good news for the overall industry but not for specific cryptocurrencies. Thanks to the QLDB technology, the Managed Blockchain ordering service will store a complete history of transactions, making it easier for clients to track and recover transaction history when needed. Amazon Personalize can make recommendations, personalise search results, and segment customers for direct and personalised marketing through email or push notifications.
Amazon is diving deeper into healthcare with the launch of Amazon Comprehend Medical, a machine-learning service created to help providers and other healthcare stakeholders extract information from unstructured data in electronic health records (EHRs). Criteria included having two strong customer references and meeting a strict set of requirements, collectively reinforcing Trend Micro's leadership role in container security and demonstrating proven success with AWS customers.