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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