This week Grzegorz, Siegfried an myself have the pleasure to attend to Amazon re:Invent in Las Vegas, USA. So far it has been a fantastic event and a great experience. According to Andy Jassy (CEO of Amazon) the conference has about 43.000 attendees. The program is full of so many interesting breakout sessions, workshops, hackathons, hands-on labs and demos – I wish it would run for two weeks instead of just one. In this blog series we will share with you some of the things we’ve learned during the conference.
In this post I start with a list of announcements that we’ve heard in the keynote today. The key topics are Serverless, Machine Learning and IoT.
- Amazon Elastic Container Service for Kubernetes (EKS) – a managed service to run Kubernetes on AWS without needing to install and operate your own Kubernetes clusters.
- AWS Fargate – a technology for Amazon ECS and EKS to run containers without having to manage servers or clusters.
- Amazon Aurora Serverless – an on-demand auto-scaling configuration for Aurora where the database will automatically start-up, shut down, and scale up or down capacity based on the application’s needs.
- Aurora Multi-Master – a feature of Aurora edition, that adds the ability to scale out write performance across multiple Availability Zones.
- DynamoDB Global Tables – replicate your Amazon DynamoDB tables automatically across your choice of AWS regions
- DynamoDB Backup and Restore – create full backups of your DynamoDB tables data for data archival.
- Amazon Neptune – a fully-managed graph database service to build and run applications that work with highly connected datasets.
- S3 Select – a new Amazon S3 capability designed to pull out only the data you need from an object in S3 by using SQL expressions.
- Glacier Select – a new Amazon Glacier capability designed to pull out only the data you need from an object in Glacier by using SQL expressions.
- Amazon SageMaker – a fully-managed service that enables developers and data scientists to build, train, and deploy machine learning models.
- AWS DeepLens – a wireless, deep learning enabled video camera for developers.
- Amazon Rekognition Video – a deep learning powered video analysis service that tracks people, detects activities, and recognizes objects, celebrities, and inappropriate content.
- Amazon Kinesis Video Streams – securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing.
- Amazon Transcribe – an automatic speech recognition (ASR) service to add speech to text capability to applications.
- Amazon Comprehend – a natural language processing (NLP) service that uses machine learning to find insights and relationships in text.
- AWS IoT 1-Click – a service that makes it easy for cloud-connected, single-purpose devices like buttons, badge readers, asset trackers, and motion sensor to trigger AWS Lambda functions that execute a specific action.
- AWS IoT Device Management – a service to securely onboard, organize, monitor, and remotely manage IoT devices at scale.
- AWS IoT Device Defender (not GA yet) – a fully managed service that helps to secure a fleet of IoT devices by continuously auditing security policies.
- AWS IoT Device Analytics – a fully-managed service to run sophisticated analytics on massive volumes of IoT data.
- Amazon FreeRTOS – an operating system for microcontrollers that makes small, low-power edge devices easier to program, deploy, secure, connect, and manage. It is based on the FreeRTOS kernel, a popular open source operating system for microcontrollers.
- Greengrass ML Inference – run local compute, messaging, data caching, and sync capabilities for connected devices in a secure way and perform ML inference locally on them.
An exciting day and we cannot wait to try the things above in our lab and by ourself. Stay tuned!