Amazon Web Services held its 10th re:Invent conference from November 29th to December 3rd in Las Vegas, as a simultaneous in-person and virtual event. The first re:Invent for new AWS CEO Adam Selipsky saw a host of product and customer announcements targeted at fulfilling customer requirements and educating more IT developers and customers on new AWS services and features.
The AWS re:Invent recap briefing was led by Mr. Santanu Dutt, Director of Technology for South East Asia with Amazon Web Services (AWS) and Mr. Anupam Mishra, Head of AWS Technology and Solutions Architecture – India, Amazon Internet Services Pvt. Ltd. Several key AWS re:Invent 2021 announcements, new services, use cases, and applicability for customers in India were outlined.
Six new capabilities for its industry-leading ML service, Amazon SageMaker
As tech industry and digital landscape evolves, companies are looking to reinvent their businesses and customer experiences using machine learning (ML). At AWS, we believe machine learning will be the most transformative technology of our generation and AWS announced six new capabilities for Amazon SageMaker at AWS re:Invent 2021.
Benefits: Making machine learning even more accessible and cost effective
- The powerful new capabilities will include a no-code environment for creating accurate ML predictions, more accurate data labeling using highly skilled annotators, a universal Amazon SageMaker Studio notebook experience for greater collaboration across domains, a compiler for ML training that makes code more efficient, automatic compute instance selection ML inference, and serverless compute for ML inference.
o Amazon SageMaker Canvas no-code machine learning predictions: Amazon SageMaker Canvas expands access to machine learning by providing business analysts (line-of-business employees supporting finance, marketing, operations, and human resources teams) with a visual interface that allows them to create more accurate machine learning predictions on their own—without requiring any machine learning experience or having to write a single line of code.
o Amazon SageMaker Ground Truth Plus expert data labeling: Amazon SageMaker Ground Truth Plus is a fully managed data labelling service that uses an expert workforce with built-in annotation workflows to deliver high-quality data for training machine learning models faster and at lower cost with no coding required. Customers need increasingly larger datasets that are correctly labelled to train ever more accurate models and scale their machine learning deployments
o Amazon SageMaker Studio universal notebooks: Today, teams across different data domains want to collaborate using a range of data engineering, analytics, and machine learning workflows. A universal notebook for Amazon SageMaker Studio (the first complete Integrated Development Environment (IDE) for machine learning) provides a single, integrated environment to perform data engineering, analytics, and machine learning.
o Amazon SageMaker Training Compiler for machine learning models: Amazon SageMaker Training Compiler is a new machine learning model compiler that automatically optimizes code to use compute resources more effectively and reduce the time it takes to train models by up to 50%.
o Amazon SageMaker Inference Recommender automatic instance selection: Amazon SageMaker Inference Recommender helps customers automatically select the best compute instance and configuration (e.g., instance count, container parameters, and model optimizations) to power a particular machine learning model.
o Amazon SageMaker Serverless Inference for machine learning models: Amazon SageMaker Serverless Inference offers pay-as-you-go pricing inference for machine learning models deployed in production. Customers are always looking to optimize costs when using machine learning, and this becomes increasingly important for applications that have intermittent traffic patterns with long idle times.
Doing more with IoT
With the explosion of devices and cloud infrastructure moving to the edge, IoT will be a major focus area for customers. AWS announced two news services AWS IoT TwinMaker and AWS IoT FleetWise for high-growth industry sectors such as manufacturing and automotive.
- AWS IoT TwinMaker – A cloud services based digital twins capability that transforms industrial operations
- Digital twins are virtual representations of physical systems, regularly updated with data to generate immediate insights about the operational state of the environments they are designed to mimic.
- Many industrial companies have the vast troves of data about their facilities required to build digital twins, but creating and managing them is hard, even for the most technically advanced organizations, so the majority are unable to use them.
- AWS IoT TwinMaker makes it faster and easier for companies to create digital twins of buildings, factories, industrial equipment, production lines, and any other physical system, helping them to do things like optimize operations, increase production output, improve equipment performance, as well as react more quickly and accurately when issues occur.
- This service is available to preview in the AWS Asia Pacific (Singapore) Region with availability in additional AWS Regions coming soon.
- AWS IoT FleetWise- Helping car manufacturers make better, safer vehicles
- Car makers will benefit from AWS IoT FleetWise, a new service designed to make it easier and more cost effective to collect and transfer vehicle data to the cloud in near-real time.
- Manufacturers have been collecting data from standard vehicle sensors for years to improve vehicle quality and safety, but as these sensors get more advanced, they also generate a lot more data. Today’s sensors can produce up to two terabytes of data an hour per vehicle (roughly equivalent to 1,000 hours worth of movies) making the cost of transferring it to the cloud hugely expensive.
- With AWS IoT FleetWise, manufacturers will get the advantage to collect and organize data from vehicles, regardless of make or model, and standardize it for analysis in the cloud.
- This will help them to diagnose issues in individual vehicles, analyze vehicle fleet health to help reduce potential recalls or safety issues, and use analytics and machine learning to improve advanced technologies such as autonomous driving.