Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The year 2021 marks a memorable milestone for Amazon Web Services (AWS) as it celebrates both re: Invent's 10th anniversary as well as its 15th anniversary.
The AWS re:Invent conference announced numerous tools and services for developers in 2019. This year, the developers at AWS paid special attention to machine learning development. In this article, we list down the top announcements on machine learning services at AWS re:Invent 2019. Amazon Augmented AI or A2I provides built-in human review workflows for common machine learning use cases, such as content moderation and text extraction from documents, which allows predictions from Amazon Rekognition and Amazon Textract to be reviewed easily. This feature makes it easy for building and managing human reviews for machine learning applications.
Machine Learning and data science life cycle involved several phases. Each phase requires complex tasks executed by different teams, as explained by Microsoft in this article. To solve the complexity of these tasks, cloud providers like Amazon, Microsoft, and Google services automate these tasks that speed up end to end the machine learning lifecycle. This article explains Amazon Web Services (AWS) cloud services used in different tasks in a machine learning life cycle. To better understand each service, I will write a brief description, a use case, and a link to the documentation. In this article, machine learning lifecycle can be replaced with data science lifecycle.
This year the annual re:invent conference organized by AWS was virtual, free and three weeks long. During multiple keynotes and sessions, AWS announced new features, improvements and cloud services. Below is a review of the main announcements impacting compute, database, storage, networking, machine learning and development. On the very first day of the conference, Amazon announced EC2 Mac instances for macOS, adding after many years a new operating system to EC2. This is mainly targeted to processes that only run on Mac OS, like building and testing applications for iOS, MacOS, tvOS and Safari.
In a follow-up to new compute, network and data service offerings announced by Amazon Web Services (AWS) CEO Adam Selipsky, AWS vice president of AI, Swami Sivasubramanian, pulled the covers off some updates to database, machine learning and serverless offerings. Taking a cue from Selipsky's theme of simplifying AWS' array of services in order to make them easier to consume for developers and enterprises, Sivasubramanian announced three new updates to AWS' plethora of database offerings. They include a new managed database service for business applications that allows developers and enterprises to customise the underlying database and operating system; a new table class for Amazon DynamoDB designed to reduce storage costs for infrequently accessed data; and a service that uses machine learning to better diagnose and remediate database-related performance issues. The new managed database service, Amazon RDS (Relational Database Service) Custom, is aimed at customers whose applications require customisation at the database level and thus are responsible for administrative tasks such as provisioning, database setup, patching and backups that take up a lot of time, Sivasubramanian said. Amazon RDS Custom automates these administrative processes while allowing customisation to the database and underlying operating system these applications require, Sivasubramanian said.