In this Blog I will quickly take you through the newly launched products and services by AWS in this recent re:Invent 2017. This re:Invent was all about the capabilities of Machine Learning and IoT in cloud. Andy Jassy launched a plethora of services including: AWS EKS, a fully managed Kubernetes service; AWS Fargate, a service to run containers without managing servers; Amazon Aurora Multi-Master; Amazon Aurora Serverless; DynamoDB Global Tables and on-demand backup; Amazon Neptune, a fully managed graph database; and AWS S3 Select and Glacier Select, allowing SQL-like queries to retrieve only a subset of data stored within objects. Let’s Giddy Up to look all the services.
Amazon ECS for Kubernetes (EKS)
Amazon Elastic Container Service for Kubernetes (Amazon EKS) is a managed service that makes it easy for you to run Kubernetes on AWS without needing to become an expert in operating Kubernetes. Amazon EKS fully manages the availability and scalability of the Kubernetes control plane for each cluster. Amazon EKS automatically performs all the cluster management operations, such as handling version upgrades, scaling the Kubernetes masters and etcd persistence layer, and detecting and replacing unhealthy masters.
AWS Fargate is a technology for deploying and managing containers without having to manage any of the underlying infrastructure. You no longer have to provision, configure, and scale clusters of virtual machines to run containers. Simply upload your container image, specify resource requirements, and Fargate launches containers for you in seconds. AWS Fargate enables you to focus on designing and running the application, not the infrastructure.
Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports the popular graph query languages Apache TinkerPop Gremlin and W3C’s SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets.
Amazon DynamoDB Global Tables
Global Tables builds upon DynamoDB’s global footprint to provide a fully managed multi-region, multi-master global database that provides fast local read and write performance for massively scaled applications with globally dispersed users. Global Tables handles the difficult work of automatically replicating data between regions and resolving update conflicts, enabling developers to focus on the application logic when building globally distributed applications.
Amazon Aurora Serverless
AWS took it to the whole new level by launching this service.
Amazon Aurora Serverless is an on-demand auto-scaling configuration for Amazon Aurora, where the database will automatically start up, shut down, and scale up or down capacity based on your application’s needs. Aurora Serverless enables you to run your relational database in the cloud without managing any database instances or clusters.
Aurora Serverless is built for applications with infrequent, intermittent or unpredictable workloads. Example include online games, low-volume blogs, new applications where demand is unknown, and dev/test environments that don’t need to constantly run. Current database solutions require a significant provisioning and management effort to adjust capacity, leading to worries about over- or under-provisioning of resources.
With Aurora Serverless, you can optionally specify the minimum and maximum capacity that your application needs, and only pay for the resources you consume. The benefits of serverless computing are now available in the world of relational databases.
This service opens up a whole new world in Machine Learning. After this service more and more companies will be attracted towards AWS.
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at scale. Amazon SageMaker includes three modules: Build, Train, and Deploy. The Build module provides a hosted environment to work with your data, experiment with algorithms, and visualize your output. The Train module allows for one-click model training and tuning at high-scale and low cost. The Deploy module provides a managed environment for you to easily host and test models for inference securely and with low latency. Amazon SageMaker removes the complexity that holds back developer success with machine learning.
Amazon Rekognition Video
Amazon Rekognition Video is a deep learning powered video analysis service that tracks people, detects activities, and recognizes objects, celebrities, and inappropriate content. Amazon Rekognition Video can detect and recognize faces in live streams. Rekognition Video analyzes existing video stored in Amazon S3 and returns specific labels of activities, people and faces, and objects with time stamps so you can easily locate the scene. It can also perform facial recognition on live video from Amazon Kinesis Video Steams. For people and faces, it also returns the bounding box, which is the specific location of the person or face in the frame.
Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to their applications. Using the Amazon Transcribe API, you can analyze any audio files stored in a common format (WAV, MP3, etc.) in Amazon Simple Storage Service (S3) and have the service return a text file of all the transcribed speech.
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; and automatically organizes a collection of text files by topic. You can use the Amazon Comprehend APIs to analyze text and use the results in a wide range of applications including voice of customer analysis, intelligent document search, and content personalization for web applications.
AWS DeepLens is a Deep-Learning enabled wireless video camera that pairs an HD camera developer kit with a set of sample projects to help developers learn machine learning concepts. DeepLens comes pre-loaded with sample projects that provide a practical, hands-on learning experience to get you started with computer vision and deep learning.
AWS IOT 1-Click
AWS IoT 1-Click is a service that makes it easy for simple devices to trigger AWS Lambda functions that execute a specific action. With AWS IoT 1-Click you can choose the action for your device by selecting one of the predefined AWS Lambda functions for common actions like sending emails or SMS messages, or you can select from Lambda functions you have created yourself using your own Lambda code.
AWS IOT Device Management
AWS IoT Device Management is an AWS IoT service that provides device management capabilities which makes it easy to securely onboard, organize, monitor, and remotely manage IoT devices at scale throughout their lifecycle.
AWS IOT Device Defender
AWS IoT Device Defender is a fully managed service that helps you secure your fleet of IoT devices. AWS IoT Device Defender continuously audits the security policies associated with your devices to make sure that they aren’t deviating from security best practices. AWS IoT Device Defender also lets you monitor devices for behavior that deviates from what you have defined as appropriate behavior for each device.
AWS IOT Analytics
AWS IoT Analytics is a fully-managed IoT analytics service that collects, processes, enriches, stores, and analyzes IoT device data at scale.
Amazon S3 Select
Most applications have to retrieve the complete set of objects and then filter out just the required data. With Amazon S3 Select, applications can offload the heavy lifting of filtering and accessing data inside objects to the Amazon S3 service to retrieve only a subset of data from an S3 object instead of retrieving the entire object. By reducing the volume of data that has to be loaded and processed by your analytics applications, S3 Select can improve the performance of most applications that frequently access data from S3 by up to 400%.
Amazon Glacier Select
Amazon Glacier Select is a new way to query archived data in Amazon Glacier. Amazon Glacier Select allows queries to run directly on data stored in Amazon Glacier, retrieving only the data you need out of your archives to use for analytics. This allows you to reduce total cost of ownership while massively extending your data lake into cost-effective archive storage.
With Amazon Glacier Select, you can now provide a SQL query and an Amazon Glacier archive where you want the query to be applied. You specify how soon you need results based on three options: Expedited Retrievals take 1-5 minutes, Standard Retrievals take 3-5 hours, and Bulk Retrievals take up to 12 hours. You are notified when a query is complete with Amazon Simple Notification Service (SNS), and you can specify the Amazon S3 bucket where you want the output results to be stored.
Using Amazon Glacier Select, you can now perform operations like auditing and pattern matching easily, over large amounts of data, which may be archived in Amazon Glacier. For example, you can use Amazon Glacier Select to find and retrieve only records matching a particular account or only billing data for a particular customer. You can also integrate Amazon Glacier Select APIs in your application, where it can be used to expand query over archive capability to many more use cases like machine learning and Big Data.
Additional information on the AWS re:Invent product launches and service upgrades can be found on the AWS News Blog.