Amazon subsidiary AWS has come a long way since its debut back in 2006. From introducing the concept of cloud computing at a time where it barely existed, AWS now offers over 200 fully-featured services to millions of customers on a global scale but newly appointed AWS CEO Adam Selipsky believes that there is still room to grow.
In the recently concluded AWS re:Invent conference, the global cloud computing community who attended both in person and virtually bore witness to the new innovations and services launched by AWS. The announcements included new services on Internet of Things (IoT), storage and analytics, cloud migration, managed databases, wide area networks, and web application UI development.
In a subsequent dialogue session, AWS Southeast Asia director of technology Santanu Dutt shared his observations that cloud adoption is already in full swing across the board in all segments: from budding new startups, established enterprises, and even small and medium-sized businesses (SMBs). He also explained that based on investor trends, the venture capital (VC) funding in Southeast Asia is expected to hit new heights.
“There will be more born-in-the cloud startups in the region, and AWS will continue supporting these startups at every stage of their lifecycle. We are proud of our strong startup heritage and the role we have played in the growth of so many Southeast Asian unicorns such as Grab, Traveloka, Tokopedia, and our two customers who have achieved unicorn status this year – Carsome and Carro,” Dutt said.
Although cloud computing has played a significant role in transforming ASEAN businesses and public sectors, there are also new technologies that are geared to give customers in the region a clear advantage. Beyond cloud migration, enterprises are diversifying the type of workloads on the cloud like Singapore’s stock exchange SGX using Amazon’s managed blockchain service to reduce trade settlement time.
The company’s machine learning service Amazon SageMaker, for example, is entering 2022 with six new capabilities behind it – the no-code machine learning prediction environment “Canvas”, fully managed expert data labeling “Ground Truth Plus”, automated machine learning model compiler “Training Compiler”, instance and configuration optimization “Inference Recommender”, pay-as-you-go pricing for deployed machine learning models “Serverless Inference”, and a complete integrated development environment for machine learning “Studio.”
In tandem with these new features are two new initiatives designed to make machine learning more accessible to interested individuals, the AWS AI and ML Scholarship education program and Amazon SageMaker Studio Lab. The scholarship will teach underserved students foundational ML concepts through AWS DeepRacer and the new AWS DeepRacer Student League. Meanwhile, the Studio Lab is essentially a free Amazon SageMaker version in public access and can build, train, and deploy ML models.
