Andy Jassy has finished presenting the 7th Annual AWS Re:Invent keynote and he didn’t disappoint. Like in years past, their are a bunch of new product announcements such as:
- Amazon FSx for Windows File Server
- Amazon FSx for Lustre
- Amazon DynamoDB On-Demand
- Amazon Elastic Inference
- SageMaker Ground Truth
- SageMaker RL
- AWS DeepRace
- Amazon Personalize
- Amazon Forecast
Amazon FSx for Windows File Server
Amazon FSx for Windows File Server fits all of these needs, and more. It was designed from the ground up to work with your existing Windows applications and environments, making lift-and-shift of your Windows workloads to the cloud super-easy. You get a native Windows file system backed by fully-managed Windows file servers, accessible via the widely adopted SMB (Server Message Block) protocol. Built on SSD storage, Amazon FSx for Windows File Server delivers the throughput, IOPS, and consistent sub-millisecond performance that you (and your Windows applications) expect.
Amazon FSx for Lustre
Amazon FSx for Lustre, designed to meet the needs of these applications and others that you will undoubtedly dream up. Based on the mature and popular Lustre open source project, Amazon FSx for Lustre is a highly parallel file system that supports sub-millisecond access to petabyte-scale file systems. Thousands of simultaneous clients (EC2 instances and on-premises servers) can drive millions of IOPS (Input/Output Operations per Second) and transfer hundreds of gigibytes of data per second.
Amazon DynamoDB On-Demand
Amazon DynamoDB on-demand, a flexible new billing option for DynamoDB capable of serving thousands of requests per second without capacity planning. DynamoDB on-demand offers simple pay-per-request pricing for read and write requests so that you only pay for what you use, making it easy to balance costs and performance. For tables using on-demand mode, DynamoDB instantly accommodates customers’ workloads as they ramp up or down to any previously observed traffic level. If the level of traffic hits a new peak, DynamoDB adapts rapidly to accommodate the workload.
Amazon Elastic Inference
Amazon Elastic Inference supports popular machine learning frameworks TensorFlow, Apache MXNet and ONNX (applied via MXNet). Changes to your existing code are minimal, but you will need to use AWS-optimized builds which automatically detect accelerators attached to instances, ensure that only authorised access is allowed, and distribute computation across the local CPU resource and the attached accelerator. These builds are available in the AWS Deep Learning AMIs, on Amazon S3 so you can build it into your own image or container, and provided automatically when you use Amazon SageMaker.
SageMaker Ground Truth
Amazon SageMaker Ground Truth can optionally use active learning to automate the labeling of your input data. Active learning is a machine learning technique that identifies data that needs to be labeled by humans and data that can be labeled by machine. Automated data labeling incurs Amazon SageMaker training and inference costs, but it can help to reduce the cost (up to 70%) and time that it takes to label your dataset over having humans label your complete dataset.
Amazon SageMaker RL builds on top of Amazon SageMaker, adding pre-packaged RL toolkits and making it easy to integrate any simulation environment. As you would expect, training and prediction infrastructure is fully managed, so that you can focus on your RL problem and not on managing servers.
Today, you can use containers provided by SageMaker for Apache MXNet and Tensorflow that include Open AI Gym, Intel Coach and Berkeley Ray RLLib. As usual with Amazon SageMaker, you can easily create your own custom environment using other RL libraries such as TensorForce or StableBaselines.
AWS DeepRacer includes a fully-configured cloud environment that you can use to train your Reinforcement Learning models. It takes advantage of the new Reinforcement Learning feature in Amazon SageMaker and also includes a 3D simulation environment powered by AWS RoboMaker. You can train an autonomous driving model against a collection of predefined race tracks included with the simulator and then evaluate them virtually or download them to a AWS DeepRacer car and verify performance in the real world.
Amazon Personalise is a machine learning service that makes it easy for developers to create individualised recommendations for customers using their applications.
Machine learning is being increasingly used to improve customer engagement by powering personalised product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the machine-learning capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organisations today due to the complexity of developing machine learning functionality. Amazon Personalise allows developers with no prior machine learning experience to easily build sophisticated personalisation capabilities into their applications, using machine learning technology perfected from years of use on Amazon.com.
Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. For example, the demand for a particular color of a shirt may change with the seasons and store location. This complex relationship is hard to determine on its own, but machine learning is ideally suited to recognise it. Once you provide your data, Amazon Forecast will automatically examine it, identify what is meaningful, and produce a forecasting model capable of making predictions that are up to 50% more accurate than looking at time series data alone.