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!