Recap of Swami Sivasubramanian’s Keynote Announcements at AWS re:Invent 2021

What To Expect From AWS reInvent 2021

Amazon Web Services (AWS) has announced a heap of features and services to make technologies like Machine Learning more effective and economical, along with a new USD $ 10 million scholarship programme for careers in Machine Learning (ML).

During his 2-hour keynote session at re:Invent 2021, which is in Day-3 of returning to Las Vegas after a one-year interval due to the pandemic, Vice President (VP), Amazon AI at AWS, Swami Sivasubramanian, revealed new solutions to make Machine Learning more approachable and inexpensive, in addition to new training programmes to further democratize the technology and make it simpler to experiment with. Also, AWS announced several new capabilities for its Machine Learning service i.e. Amazon SageMaker. This combines strong new capabilities, which include a no-code environment for building accurate ML predictions, more precise data labelling utilizing highly skilled annotators, and a universal Amazon SageMaker Studio notebook experience for better association across domains. We hereby present below, a summary of Sivasubramanian’s biggest announcements:

  • Amazon DevOps Guru for RDS tool lets you automatically detect, diagnose and resolve complicated database-related (Amazon Aurora databases) issues within minutes. Also, the DevOps Guru for RDS can help rectify a wide range of issues, such as over-exploitation of host resources, database bottlenecks or misbehavior of SQL queries. Whenever an issue is detected, users can view them either through the DevOps Guru console or via notifications from Amazon EventBridge or Amazon Simple Notification Service (SNS).
  • AWS Database Migration Service Fleet Advisor lets you accelerate database migration with automated inventory and migration recommendations. This tool is specifically designed to help make it easier and quicker to get your data to the cloud and match it with the appropriate database service. DMS Fleet Advisor spontaneously builds an inventory of your on-prem database and analytics service by streaming data from on-prem to Amazon S3.
  • New SageMaker Studio Notebook service allows users to access a broad range of data sources and conduct data engineering, analytics, and Machine Learning workflows in one notebook. Currently, Amazon SageMaker Studio has the capability to integrate directly to EMR, the company’s Hadoop-based service that grants access to frameworks such as Spark, Presto, MapReduce, and Hive. Now, SageMaker Studio users can build, terminate, manage, discover and connect to EMR clusters directly from within their SageMaker Studio environment, which would in turn streamline workflows for data scientists.
  • Amazon Sagemaker Studio Lab is a free service for students and other learners or developers to experiment and learn Machine Learning. There are things like JupyterLab IDE, 15 GB of storage, and model can be trained on GPUs. After training the model, the user can also deploy the model in AWS Infrastructure just by one-click using Sagemaker capabilities.
  • Amazon SageMaker Ground Truth Plus allows users to deliver high-quality training databases fast, with no necessity to write a single line of code. Basically, this is a professional services version of SageMaker Ground Truth, which already exists. This new service empowers users to associate themselves with a pool of expert data labelers who have been curated by AWS, and to have the data labeling process directly incorporated with their SageMaker environment. Also, this new service offering can bring down data labeling costs by up to 40%.
  • Amazon SageMaker Platform is getting 3 new innovations:
    • SageMaker Training Compiler is a new feature that can accelerate the training of deep learning models by up to 50% through more efficient use of GPU instances.
    • SageMaker Inference Recommender helps users to choose the best available compute instance and configuration to deploy Machine Learning models for ideal inference performance and cost. This new feature can reduce the time to deploy from weeks to hours.
    • SageMaker Serverless Inference is a new inference option that empowers users to easily deploy machine learning models for inference without having to configure or manage the underlying infrastructure. This new feature can lower the cost of ownership with pay-per-use pricing.
  • Amazon Kendra Experience Builder allows you to deploy a fully functional and customizable search experience with Amazon Kendra in just a few clicks, with absolutely no necessity for any coding or Machine Learning experience. Experience Builder service delivers an intuitive visual workflow to swiftly build, customize and initiate your Kendra-powered search application, safely on the cloud. You can begin with the ready-made search experience template in the builder, that can be tailored by simply dragging and dropping the components you require, like filters or sorting.
  • Amazon Lex Automated Chatbot Builder is a new capability which reduces the time and effort it takes for customers and partners to design a chatbot from weeks to hours, by simply automating the process using existing conversation transcripts. It is indeed an easy and intuitive way of designing chatbots, by employing advanced natural language comprehension driven by deep learning techniques. Amazon Lex enables you to build, test and deploy chatbots and virtual assistants on contact center services (i.e. Amazon Connect), websites and messaging platforms (e.g. Facebook Messenger). The automated chatbot designer widens the usability of Amazon Lex to the design phase. It utilizes Machine Learning to render an initial bot design that you can then refine and initiate conversational experiences quicker.

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Recap of Adam Selipsky’s AWS re:Invent 2021 Keynote Announcements

What To Expect From AWS reInvent 2021

CEO of Amazon Web Services (AWS), Adam Selipsky initiated day 2 of AWS re:Invent 2021 with a keynote presentation filled with several exciting announcements, status reports, and long-term vision-settings for the AWS Cloud platform. From Selipsky’s keynote, it is very evident that AWS has ambitious objectives, in addition to the potency to carry through. One thing that Selipsky emphasized, which could be surprising to many, is that we are still in the early stages of the Cloud. Andy Jassey, CEO of Amazon evaluated back in the month of April that only less than 5% of all IT spend takes place on the Cloud. In a nutshell, there are still enormous opportunities available to improve and upgrade IT infrastructure.

And, as Selipsky described, AWS continues to be the perfect place for this to materialize. Now especially, it makes sense to retain indispensable workloads on the AWS and innovate with the support of the platform. AWS continues to make huge investments in its already robust suite of cloud-native products, solutions and tools. This year, Selipsky announced a stack of advancements to existing technologies and absolutely new services that will empower more business organizations to benefit from the power of Cloud Computing. We hereby present below, a summary of Selipsky’s biggest announcements:

  • A brand new Graviton3 Arm Server Chip which operates 25% faster than Graviton2, consumes 60% less energy, and provides 3x faster performance for Machine Learning (ML) workloads and 2x faster for cryptographic workloads.
  • New C7g Instances energized by the Graviton3 Arm Server Chip which will best support compute-intensive workloads.
  • New EC2 Trn1 instances that will expedite and improve deep learning models with a network bandwidth of 800 Gbps and finer total price for performance.
  • A novel AWS Mainframe Modernization service that enables business organizations to migrate, modernize, and flawlessly run mainframe workloads on AWS. The solution has already proven to reduce the time taken for by 67%.
  • A novel AWS Private 5G Service that will permit users to setup, manage and scale their own private mobile networks within days along with automatic configuration, zero per-device charges, and collaborative spectrum operation. AWS in turn provides all the software, hardware, and SIM’s required for the Private 5G service, making it a comprehensive solution that is the first of its type.
  • Key Updates to AWS Lake Formation which include: Security at both Row and Cell-level, that will in return enable users to restrict access to specific data and only disclose sensitive information to approved users. Transactions for Governed Tables in Lake Formation excludes the need for batching updates.
  • Serverless & On-Demand Analytics for Amazon Redshift, MSK, EMR, and Kinesis, which implies business organizations, can now carry out Advanced Analytics without having to worry about provisioning capability or managing servers.
  • A novel Amazon SageMaker Canvas Service which offers a point and click interface for business users and analysts to initiate Machine Language (ML) predictions without needing to write any kind of code.
  • A novel AWS IoT TwinMaker Service which makes the process of creating and utilizing digital twins for real-world systems simpler.
  • A novel AWS IoT FleetWise Service that rationalizes how automobile firms acquire data from large fleets, which will be very vital in the forthcoming autonomous self-driving age.

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