How will Digital Transformation Reshape Financial Services in 2022

Digital Transformation Reshape Financial Services

The Financial Services industry has gone through a swift and substantial digital transformation journey in the FY 2020-21 via. cognitive automation, conversational servicing, digital embracement, video KYC, open banking etc.

Presently, 55% of financial service organizations have extended resiliency plans to future-proof their business and enhance profitability, innovation rates and cost efficiencies by greater than 20% in contrast to their peers. As per statistics, the global digital payment market size is anticipated to grow at a CAGR of 15.2% between 2021-2026. Also, by 2023, 90% of organizations worldwide are expected to emphasize on investments in digital tools to supplement physical spaces and assets with digital experiences.

The demand for digital technologies is gaining prominence in the financial services industry and going forward innovation is going to play a major role in accelerating the origination of new digital financial products.

1. Current Lending Landscape

(i) COVID impact accelerating Digital Transformation: The impact of COVID-19 on Digital Transformation in Financial Services is detailed through the following steps:

   (a) Greater Automation to Create Contactless Services: The increased tendency of customers towards buying contactless services has led financial organizations to adopt automation. e.g., applying for a loan

   (b) Increased Focus on Technology: The push towards Digital Transformation in Financial Industry has led several organizations to review their investments in the technological sector. This in turn has fueled innovation. e.g., integration of AI in Lending.

   (c) Focus on Customer Experience: Recently, AI based Lending has allowed lenders to shorten the loan process by applying digital transformation. An Automated Loan Approval process lets lending organizations quickly close more qualified loans.

   (d) Maximizing Operational Efficiency & Minimizing Costs: Digital Transformation in Financial Industry is continuing to assure cost-efficient operations while remaining extremely competitive. Optimizing business efficiencies and operations across the supply chain is financial services organizations’ top digital primacy to reduce costs and maximize revenue.

   (e) Increased Investments in Cybersecurity: With more reliance on digital platforms for performing traditional operational processes, there is a huge transaction of critical information. Cybersecurity investments are set to grow high as organizations invest heavily in new advanced technologies and top-level cyberattacks propagate.

(ii) Number of Lenders focused on Small Businesses: COVID-19 has expedited the adoption of Digital Finance Transformation and generated large opportunities in consumer lending. Now, there is a significant increase in lenders, available as options for small businesses to borrow.

(iii) Regional Banks are now following FOMO: Seeing the results of the new lending age, regional banks are now following the digital transformation bandwagon. Currently, there are big investments from regional banks, credit unions and fintech companies, focused on upgrading their existing banking system to digital lending technology to facilitate faster lending.

2. Key Lending Predictions

(i) More Lenders will evolve, catering focused on Specific Lending: To differentiate from traditional lenders and capture strong commercial loan growth opportunities, lenders are increasingly targeting specific industry sectors such as healthcare, transportation etc.

(ii) Emphasize on Open Finance: Open Finance is based on data-sharing principles that can entitle banks to provide a wide range of possibilities to their clients that are especially appropriate to their requirements. With Open Finance, consumers could feasibly access more powerful private mortgages, pension funds, savings systems, credit/loan, insurance, and investments.

The map below shows countries that are effectively implementing their Open-Finance infrastructure:

>Digital Transformation Reshape Financial Services

(iii) Increased Spending on Technology and Teams: Recently, a rise in technology and teams spend is seen as a response to the pandemic. Financial service organizations are witnessing continuous disruption with expedited spending as technology speeds forward at a quick pace. Consequently, it is a mandate to stay highly competitive.

(iv) Integration of API’s from Data Aggregators: API providers build, expose, and operate API’s. e.g., 1. Data Providers API, 2. Machine Learning via an API. API integration from Data Aggregators allows for seamless integration and a quicker turnaround time.

(v) Apps give enhanced visibility for Borrowers: Given its fast-paced nature, lending apps exclude the need for a physical loan application process. These apps connect the lender, and borrower, offering wholesome loan servicing.

(vi) Significantly more accurate default predictions using more data: Data is fundamental to business intelligence and with digital transformation providing more access to useful data, higher is the capability to accurately predict the probability of default.

(vii) Customized Lending Rates for everyone based on your Data: Better data enables better customer understanding which empowers banks to provide their customers with personalized lending.

(viii) Digital Assistants will help both Lenders and Borrowers: Digital Assistants or Mortgage Chatbots greatly assist lenders in loan servicing and play a crucial role in constituting the decision-making process for borrowers. They are known to significantly reduce the time and effort in decision-making and processing loan offers.

(ix) Increased Automation across several workflows to Reduce Costs: Increased automation across workflows considerably minimize the probability for human error, thereby aiding to ensure consistency, process adherence, compliance, and greater security. In well-knit digital lending organizations, an Automated Digital Loan Approval process virtually eradicates the wearisome sorting of paper and electronic files, besides reducing manual data entry.

(x) Decentralized Lending (DeFi): DeFi is a peer-to-peer financial service working on Blockchain platform and as one of the financial services they do have the option to facilitate loans. These platforms offer loans to users at competitive rates in comparison to the traditional lending platforms.

(xi) Lending in Metaverse: The metaverse is a concept of a consistent, online, 3D universe blending multiple virtual spaces and letting users work, engage, and interact together in these 3D spaces. The concept allows our avatars to own entities such as properties, and other artifacts. For us to own these, for e.g., a loan requirement, there are lending institutions who can offer the same.

Conclusion: Recently, banks have accelerated the pace of Digital Transformation in Financial Services owing to the rapid growth of technology. Also, many AI Based Lending apps have emerged with the extensive use of smartphones. Both AI and Automation have just started to explore the boundaries of profitability, efficiency, expenses, and end-user experience in the lending landscape. This year, Digital Transformation in Financial Industry will enable banks to build the next generation experience.

INFOGRAPHIC: PUBLIC SECTOR TECHNOLOGY TRENDS TO WATCH FOR IN 2022

This infographic provides an overview of public sector technology trends in 2022 that can guide public sector leaders in accelerating digital transformation and minimizing disruption risks. Our visual briefing determines key trends that lie in the spaces between several emerging technologies and the impact they are likely to bring about to the public sector. Over the next 12-18 months, these technology trends could disrupt the way public sector organizations engage with citizens, execute tasks, and prepare for the future.

Cloud-Computing-Market-Overview-2017

AWS re:Invent 2021: Our Predictions Vs Announcements for ML Services

AWS re:Invent 2021: Our Predictions Vs Announcements for ML Services

Based on the current trends and advancements in the technology industry, in addition to several other factors, we made certain predictions about new services / features that were likely to be launched at the AWS re:Invent 2021 annual conference. The table below presents a wrap-up of all our predictions about the event in comparison with the actual announcements made by AWS:

S.No

Idexcel’s Predictions

AWS re:Invent 2021 Announcements

1

Release of new generation ec2 instances for faster Machine Learning Training and Inference, which will offer a better Price Performance Ratio.

AWS announced 3 new Amazon EC2 instances powered by AWS-designed chips. They are as follows:

(i) Amazon EC2 C7g instances powered by new AWS Graviton3 processors that provide up to 25% better performance for compute-intensive workloads over current generation C6g instances powered by AWS Graviton2 processors.

(ii) Amazon EC2 Trn1 instances powered by AWS Trainium chips which provide the best price performance and the fastest time to train most Machine Learning models in Amazon EC2.

2

Amazon Textract will soon penetrate the market by providing extraction solutions that are domain specific, covering specific types of document extraction solutions. We may see examples of specific types of documents that will be extracted.

Amazon Textract had announced specialized support for automated processing of identity documents. Users can now swiftly and accurately extract information from IDs (eg. U.S. Driver Licenses & Passports) which have varying templates or formats.

3

Improvements in Lex are likely to be out later this year or early next year, with the recent acquisition of Wickr.

AWS announced the Amazon Lex Automated Chat Bot Designer (in Preview), a new feature that simplifies the process of chatbot training and design by bringing in a level of automation to it.

4

A range of Automation options within AWS Service are likely to be announced.

Amazon SageMaker Inference Recommender – A new capability of SageMaker introduced at AWS re:Invent 2021, which lets users choose the best available compute instance and configuration to deploy machine learning models for optimal inference performance and cost. Also, it minimizes the time taken to obtain Machine Learning (ML) models in production by automating performance benchmarking and load testing models across SageMaker ML instances. Users can now utilize Inference Recommender to deploy their model to a real-time inference endpoint that delivers the finest performance at a meanest cost.

Top Announcements of AWS re:Invent 2021

Top Announcements of AWS re:Invent 2021

Amazon made several announcements at AWS re:Invent 2021 that include new services and enhancements, whilst their partners fostered their offerings, turning the annual conference into one of the most significant events of the cloud technology marketplace. The following are the top announcements of this year.

1. AWS Private 5G

AWS Private 5G is a new managed service that helps organizations set up and scale private 5G mobile networks in their respective facilities within days instead of months. With the minimal number of clicks in the AWS console, customers define where they want to build a mobile network and the network capacity required for their devices. Subsequently, AWS delivers and maintains the small cell radio units, servers, 5G core, and radio access network (RAN) software, and subscriber identity module (SIM) cards required to build a private 5G network and link devices. Also, AWS Private 5G automates the setup and deployment of the network and scales capacity on-demand to support supplementary devices and enhanced network traffic. There are absolutely no per-device costs or fees to be paid in advance with AWS Private 5G and customers only have to pay for the network capacity and throughput they request.

2. Amazon SageMaker Canvas

Amazon SageMaker Canvas leverages the most powerful AutoML technology from Amazon SageMaker, which spontaneously trains and builds models depending on your dataset. This lets SageMaker Canvas to determine the best model based on your dataset so that you can generate single or bulk predictions. It supports numerous problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. These problem types allow you to address business-critical use cases, such as fraud detection, churn reduction, and inventory optimization without the need of having to write a single line of code. Also, this capability offers an intuitive user interface to swiftly connect to and access data from contrasting sources and prepare data for building ML models.

3. Amazon DevOps Guru for RDS

Amazon DevOps Guru for RDS is a new capability powered by Machine Learning for Amazon Relational Database Service (RDS) that systematically identifies and diagnoses database performance and operational issues, enabling users to address bottlenecks in minutes instead of days. This feature of Amazon DevOps Guru, uncovers issues related to operation and performance for all Amazon RDS engines and dozens of other resource types. Also, DevOps Guru for RDS extends upon the existing capabilities of DevOps Guru to determine, diagnose and offer correction recommendations for a range of issues related to performance of database, such as resource over-utilization and misbehavior of SQL queries. Whenever an issue arises, DevOps Guru for RDS instantly notifies developers and DevOps engineers by providing diagnostic information, info on the extent of the issue, and intelligent restoration recommendations to assist customers to promptly resolve the issue.

4. Amazon SageMaker Ground Truth Plus

Amazon SageMaker Ground Truth Plus enables users to create high-quality training datasets with ease, with no need to build labeling applications and manage the labeling workforce on your own. Consequently, you don’t even need to have deep expertise in Machine Learning or extensive knowledge of workflow design and quality management. You simply provide data together with labeling requirements and Ground Truth Plus sets up the data labeling workflows and manages them on your behalf in concordance with your requirements. Also, this latest service uses a multi-tier labeling workflow that includes ML techniques for active learning, pre-labeling and machine validation. This reduces the time required to label datasets for a range of use cases which include computer vision and natural language processing.

5. New EC2 instances featuring Trn1 and Graviton3 Processors

AWS Trainium-based Amazon EC2 Trn1 instances deliver the best price-performance for training deep machine learning models in the cloud for use cases like natural language processing, object detection image recognition, recommendation engines, intelligent search, etc. They support up to 16 Trainium accelerators, 800 Gbps of EFA networking throughput, and ultra-high speed intra-instance connectivity for the quickest ML training in Amazon EC2. These instances are deployed in EC2 UltraClusters that can be scaled to tens of thousands of Trainium accelerators with petabit scale and non-blocking networking. These Trn1 UltraClusters are 2.5x bigger than the former generation EC2 UltraClusters and assist as a powerful supercomputer to quickly train the most complicated deep learning models.

AWS Graviton3 Processors are the latest addition to the Graviton family of processors which are tailor-made by AWS to provide the best price-performance for workloads in Amazon EC2. They offer up to 25% better compute performance, 2x higher floating-point performance, and 2x faster cryptographic workload performance when compared to AWS Graviton2 processors. Also, Graviton3 processors render up to 3x better performance in comparison to Graviton2 processors for CPU-based machine learning workloads, along with additional support for bfloat16 and fp16 instructions. Furthermore, they support pointer authentication for improved security besides always-on 256-bit memory encryption available in AWS Graviton2.

6. Karpenter – An Open-Source High-Performance Kubernetes Cluster Autoscaler

Karpenter is an open-source, resilient, high-performance Kubernetes cluster autoscaler created with AWS. It helps improve the application availability and cluster efficiency of users by swiftly launching appropriately sized compute resources in response to the dynamic application load. Also, this autoscaler offers compute resources in a timely manner to meet your application’s requirements and will soon instinctively optimize a cluster’s compute resource footprint to minimize costs and enhance performance. Karpenter is created to work with any Kubernetes cluster irrespective of the running environment, comprising of all principal cloud providers and on-premises environments. As Karpenter is installed in a user’s cluster, it inspects the aggregate resource requests of unscheduled pods and generates decisions to institute new nodes and terminate them to minimize scheduling latencies and infrastructure expenses.

7. AWS Amplify Studio

This new visual development environment offers frontend developers with features to expedite the development of UI with minimal coding, whilst integrating Amplify’s powerful backend configuration and management capabilities. AWS Amplify Studio spontaneously translates designs made in Figma to human-readable React UI component code. Developers can now visually connect the UI components to app backend data within Amplify Studio. With regard to configuring and managing backends, Amplify Admin UI’s present capabilities will be part of Amplify Studio moving forward, offering an integrated interface to empower developers to build full-stack applications quicker. Furthermore, to speed up the UI development process, Amplify Studio provides developers a React UI library with several components such as newsfeeds, contact forms, and e-commerce cards.

8. Amazon Mask Severless (in preview)

This is a new type of Amazon MSK cluster that makes it simpler for developers to run Apache Kafka without needing to manage its capacity. Amazon MSK Serverless spontaneously provisions and scales compute and storage resources and provides throughput-based pricing, so that you can utilize Apache Kafka on demand and pay only for the data you stream and retain. With minimal clicks in the AWS management console, users can set up secure and highly available clusters that automatically scale as your application I/O scales. MSK serverless enables users to run existing applications without needing to change code or create new applications using prominent tools and APIs, as it is fully compatible with Apache Kafka. Also, this cluster supports native AWS integrations that offer capabilities like private connectivity with AWS PrivateLink, safe client access with AWS Identity and Access Management (IAM), and schema evolution control with AWS Glue Schema Registry.

9. Amazon EMR Serverless (in preview)

This is a new serverless option in Amazon EMR that makes it simple and economical for data engineers and analysts to run petabyte-scale data analytics in the cloud. As you are aware, Amazon EMR is a cloud big data platform utilized by customers to run large-scale distributed data processing jobs, interactive SQL queries, and machine learning applications using open-source analytics frameworks like Apache Hive, Apache Spark, and Presto. Now with the introduction of Amazon EMR Serverless, customers can run applications built with the aid of these frameworks with minimal clicks, without needing to configure, optimize and secure clusters. EMR Serverless instinctively provisions and scales the compute and memory resources needed by the application, and customers have to only pay for the resources they utilize. Also, with EMR Serverless, users need to just specify the open-source framework and version that they wish to use for their application and submit jobs using EMR Studio, API’s or JDBC & ODBC clients.

10. AWS Cloud WAN Preview

AWS Cloud WAN is a new managed wide-area networking (WAN) service that makes it easier for users to create, manage and track any global network that connects resources running across their cloud and on-premises environments. This service provides a simple way to link all your data centers, branch offices, and cloud resources into an integrated, centrally managed network, minimizing the operational costs and complexities involved with running a global network. Furthermore, with Cloud WAN, users can utilize a centralized dashboard and network policies to build a global network that covers multiple locations and networks, ultimately eliminating the need to individually configure and manage different networks using diverse technologies. As Cloud WAN automatically creates a global network across AWS Regions using the Border Gateway Protocol (BGP), users can now easily exchange routes round the globe.

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Recap of Werner Vogel’s Keynote Announcements at AWS re:Invent 2021

Werner Vogel's Keynote Announcements

For a top technologist habitual to keeping a sharp focus on the future, Amazon VP & CTO, Werner Vogels placed a vast array of his keynote at AWS re:Invent 2021 by looking back in time. The Chief Technology Officer at Amazon.com Inc. made several references to his first re:Invent keynote in 2012 wherein he summarized a set of fundamental rules for the development community. These included, creating APIs with explicit and well-documented failure modes and avoiding leaking implementation information at any cost.

Irrespective of Vogels’ concern over the lessons of a decade ago, he proposed plenty of new announcements for the present times day during 2-hours of keynote speech. His remarks incorporated the offering of M1 Mac minis as part of EC2 and the announcement that 30 new AWS Local Zones could be expected online for the company’s extensive datacenter network. Also, Werner Vogels spent time on 2 specific areas during his presentation at the re:Invent conference. One was related to IAM (Identity & Access Management) and its greater emphasis in the function of application programming interface calls. Another key area which Vogels geared towards was the AWS’ space-based initiative. We hereby present a summary of Vogel’s biggest announcements below:

  • Introduction of new Amazon EC2 M1 Mac Instances – The introduction of Amazon EC2 M1 Mac instances have brought great flexibility, scalability and cost benefits of AWS to all Apple developers. EC2 Mac instances are devoted Mac mini computers connected with Thunderbolt to the AWS Nitro System, letting the Mac mini appear and behave like another EC2 instance. The introduction of EC2 M1 Mac instances now allow users to access machines built around the Apple-designed M1 System on Chip (SoC). Mac developers re-architecting their apps to natively support Macs with Apple silicon, can now build and test their apps and reap all the benefits of AWS. Developers working for iPhone, iPad, Apple Watch, and Apple TV will also greatly benefit from faster builds. The new EC2 M1 Mac instances promise up to 60% better price performance and efficiency over the x86-based EC2 Mac instances for iPhone and Mac app build workloads.
  • Introduction of AWS Cloud WAN – The AWS Cloud WAN is a Managed Wide Area Networking (WAN) service that makes it simpler for users to build, manage and monitor a global network that connects resources running across their cloud and on-premises environments. AWS offers customers with various powerful, yet simple services for Cloud networking. The Cloud WAN Service provides easy means to connect your data centers, branch offices and cloud resources into an integrated, centrally managed network, minimizing the overall operational cost and complexity involved with running a global network.
  • Launch of 30 new AWS local zones in 2022 – AWS will launch over 30 new AWS Local Zones in major cities round the globe. Starting in 2022, these new AWS Local Zones will be made available in over 21 countries, that include Austria, Argentina, Australia, Brazil, Belgium, Chile, Canada, Czech Republic, Colombia, Finland, Germany, Denmark, Greece, Kenya, India, Norway, Netherlands, Philippines, Portugal, Poland, and South Africa, and join 16 Local Zones across the US, helping you to serve end-users round the globe with much lower latency. AWS’s local zones are a kind of infra deployment that places AWS computing, storage, database and other select services closer to users, industry and technology centers, typically where there is no AWS region availability.
  • Introduction of AWS Amplify Studio – AWS Amplify Studio is a visual development environment that provides frontend developers new features to accelerate UI development with less coding, whilst incorporating Amplify’s powerful backend configuration and management capabilities. Developers can visually connect the UI components to app backend data, within Amplify Studio. In order to configure and manage backends, Amplify Admin UI’s existing capabilities will be part of Amplify Studio moving forward, offering a unified interface to allow developers build full-stack apps quicker. Amplify Studio can now be used by developers to set up a backend, build UI components and connect these two together, everything within Amplify Studio. Besides containing all of Admin UI’s existing backend creation and management capabilities, Amplify Studio simplifies set up and management of app backend infrastructure such as database tables, user authentication and file storage, without the need of any cloud expertise.
  • General Availability Announcement of AWS Cloud Development Kit (CDK) v2 – Basically, the AWS CDK is an open-source framework that makes it easier to work with cloud resources using conventional programming languages such as C#, TypeScript, Java, Python and Go. The AWS Cloud Development Kit (AWS CDK) v2 is now generally available in a single package, making it easier for users to utilize the CDK and stay abreast with new versions as it evolves going forward. AWS CDK v2 amalgamates the AWS Construct Library into a single package called aws-cdk-lib, and eradicates the need of downloading individual packages for each AWS service utilized. Now, you only need to take a minimum dependency on this single package if you write your own CDK construct libraries, letting library consumers choose which exact AWS CDK version to use. As AWS CDK v2 includes only stable API’s that adhere to Semantic Versioning (semver), you can now confidently update to new minor versions. Also, according to Vogels, this newly available kit is sure to provide fixes to various issues encountered with version v1.
  • General Availability Announcement of New Construct Hub – The New Construct Hub is a registry of open-source construct libraries for making the cloud development process simpler. These Constructs are reusable building blocks of the Cloud Development Kits (CDKs). The new Construct Hub lets developers to discover and share open –source CDK libraries such as CDK constructs for the AWS Cloud Development Kit (CDK), CDK for Kubernetes (CDK8), CDK for Terraform (CDKtf) and other construct-based tools.
  • Launch of AWS re:Post: An amended Q&A Experience for the AWS Community – This is a new, question and answer (Q&A) service as part of the AWS Free Tier, led by the community of AWS employees, customers, partners. AWS re:Post is an AWS-managed Question & Answer service providing collaborative, expert-reviewed answers to the technical questions of users about AWS that substitutes the original AWS Forums. Now, community members can garner reputation points to augment their community expert status by delivering approved answers and reviewing answers from other users, aiding to continually expand public knowledge availability across all AWS services.

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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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Key Highlights of AWS re:Invent 2021

What To Expect From AWS reInvent 2021

1. Amazon WorkSpaces introduces Amazon WorkSpaces Web

Amazon WorkSpaces Web makes it easy for customers to safely provide their employees with access to internal websites and SaaS web applications without the administrative burden of appliances or specialized client software. WorkSpaces Web provides simple policy tools custom-built for user interactions while offloading common tasks like capacity management, scaling, and maintaining browser images. With WorkSpaces Web, corporate data never resides on remote devices. Websites are rendered in an isolated container in AWS, and pixel streamed to the user. The isolated browsing session provides an effective barrier against attacks packaged in the web content and avoids potentially compromised end-user devices from ever connecting with internal servers. Also, every session launches a fresh, always up-to-date, non-persistent web browser.

2. Announcing Amazon Redshift Serverless (Preview)

Amazon Redshift now provides a Serverless option (preview) to run and scale analytics without having to provision and manage data warehouse clusters. With Amazon Redshift Serverless, all users including data analysts, developers, and data scientists can now use Amazon Redshift to get insights from data within seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver the best-in-class performance for all your analytics.

3. Introducing Amazon FSx for OpenZFS

Amazon FSx for OpenZFS empowers you to launch, run, and scale fully managed file systems on AWS that replace the ZFS or other Linux-based file servers you run on-premises while helping to provide better agility and reduced costs. This new addition lets you use a popular file system without having to deal with hardware provisioning, software configuration, patching, backups, and the like. You can create a file system in minutes and begin to enjoy the benefits of OpenZFS right away viz. transparent compression, continuous integrity verification, snapshots, and copy-on-write. Even further, you get all of these benefits without having to develop the specialized expertise that has traditionally been required to set up and administer OpenZFS.

4. Amazon FSx for Lustre now supports linking multiple Amazon S3 buckets to a file system

Amazon FSx for Lustre is a service that provides cost-effective, high-performance, scalable file systems for compute workloads, making it more easier to process data residing in Amazon S3 by enabling an FSx for Lustre file system to be linked to multiple S3 buckets. Integrated with Amazon S3, FSx for Lustre enables you to easily to process S3 datasets with a high-performance file system. With today’s launch, you can link multiple S3 buckets or prefixes to a file system and your S3 datasets appear as files and directories in a single unified file system namespace.

5. Amazon FSx for Lustre now supports automatically exporting file updates to Amazon S3

Amazon FSx for Lustre is a service that provides cost-effective, high-performance, scalable file systems for compute workloads, making it much easier to process data residing in Amazon S3 by enabling your S3 bucket’s contents to be updated automatically as data is updated in an FSx for Lustre file system. Integrated with Amazon S3, FSx for Lustre empowers you to easily process S3 datasets with a high-performance file system. With today’s launch, FSx for Lustre can also automatically update the contents of the linked S3 bucket as files are added to, changed in, or deleted from the file system.

6. Introducing AWS Migration Hub Refactor Spaces – Preview

AWS Migration Hub Refactor Spaces is the new starting point for incremental app refactor that makes it very easy to manage the refactor process while operating in production. Using Refactor Spaces, customers focus on the refactor of their applications, and not the creation and management of the underlying infrastructure that makes refactoring possible. This new Migration Hub feature minimizes the business risk of evolving applications into microservices or extending existing applications with new features written in microservices.

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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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