Top 12 AI and machine learning announcements at AWS re:Invent 2021

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During its Re: Invent 2021 conference in Las Vegas this week, Amazon announced several new AI and machine learning products and updates to its Amazon Web Services (AWS) portfolio. DevOps, a touch on big data and analytics, highlights had a call summary feature for Amazon Lex and the ability in Code Guru to help discover secrets in source code.

AI is consistently embraced by Amazon as the enterprise seeks pilot automation technology to transition their businesses online. Fifty-two percent of companies have stepped up their AI adoption plans because of the Kovid epidemic, according to a study by PricewaterhouseCoopers. Meanwhile, Harris Poll found that 55% of companies have accelerated their AI strategy in 2020 and 67% expect their strategy to accelerate further in 2021.

Swami Shivsubramanian, AWS VP of Machine Learning, said in a statement, “The first we are announcing is designed to open up educational opportunities in machine learning to make it more widely accessible to anyone interested in technology.” “Machine learning will be one of the most transformative technologies of this generation. If we are going to unlock the full potential of this technology to face some of the most challenging problems in the world, we need the best brains entering this field from every background and sphere of life. “

DevOps

About a year after launching CodeGuru, an AI-powered developer tool that provides recommendations for improving code quality, Amazon unveiled the new CodeGuru Reviewer Secret Detector this week. An automated tool that helps developers find secrets in source code or configuration files such as passwords, API keys, SSH keys and access tokens, taking advantage of AI to identify hard-coded secrets as part of the secret detector code review process.

According to Amazon, the goal is to help ensure that not all new code has secrets before merging and deploying. In addition to discovering secrets, the Secret Detector AWS Secret Manager can suggest measures to protect secrets, along with Amazon’s managed service that lets customers store and retrieve secrets.

Secrets Detector is included as part of CodeGuru Reviewer, a component of CodeGuru, free of charge and is supported by most API providers including AWS, Atlassian, Datadog, Databricks, GitHub, Hubspot, Mailchimp, Salesforce, Shopify, Slack, Stripe. . Tablo, Telegram and Twilio.

Enterprise

Contact Lens, a virtual call center product for Amazon Connect that collectively transcribes calls when evaluating them, now features call summaries. By default enabled, contact lenses, Amazon’s cloud contact center service, provide transcripts of all calls made by Connect.

In a related development, Amazon has launched an automated chatbot designer in Lex, the company’s service for creating voice and text interfaces of conversation. Designers use machine learning to provide an initial chatbot design that developers can modify to create conversation experiences for customers.

And Texttrack, Amazon’s machine learning service that automatically extracts text, signatures and data from scanned documents, now supports identification documents, including licenses and passports. Without the need for templates or configurations, users can automatically retrieve accurate as well as implicit information from IDs, such as expiration date, date of birth, name and address.

Sagemaker

Sagemaker, Amazon’s cloud machine learning development platform, has received a number of enhancements this week, including a visual, no-code tool called Sagemaker Canvas. Canvas allows business analysts to create machine learning models and generate predictions once updated data becomes available, by combining datasets and training models, by browsing different data sources in the cloud or on-premises.

Sagemaker Ground Truth Plus is also new, a turnkey service that employs “expert” employees to deliver high-quality training datasets while eliminating the need for companies to operate their own labeling application. Ground Truth Plus complements improvements to Sagemaker Studio, including a new way to configure and provide a compute cluster for workload needs with the support of DevOps practitioners.

Within Sagemaker Studio, Sagemaker Interface Recommander – another new feature – automates load testing and optimizes model performance across machine learning instances. The idea is to allow MLOps engineers to run load tests against their models in a simulated environment, thus reducing the time it takes for machine learning models to get from development to production.

Developers can get free access to Sagemaker Studio through the new Studio Lab, which does not require an AWS account or billing details. Users can sign up with their email address through a web browser and start creating and training machine learning models without any financial obligation or long term commitment.

Sagemaker Training Compiler, another new Sagemaker capability, aims to accelerate the training of deep learning models by automatically compiling developers’ Python programming codes and generating GPU kernels specifically for their models. The training code will use less memory and calculation and therefore provide faster training, reduce costs and save time, Amazon says.

Sagemaker is the last serverless guess on the front, a new guess option that enables users to use machine learning models to guess without configuring or managing the underlying infrastructure. With serverless interface, Sagemaker automatically enables, scales and closes the ability to calculate based on the volume of guess requests. Customers pay only for the duration of the guess code run and the amount of data processed, not for idle time.

Calculation

Amazon also announced the Graviton 3, the next generation of its custom ARM-based chip for AI predictable applications. The AWS C7g will be available soon in instances, the company says, adding that the processors have been optimized for workloads including high-performance calculation, batch processing, media encoding, scientific modeling, advertising service and distributed analysis.

With Graviton3, Amazon debuted Trn1, a new example of training deep learning models in the cloud – including models for apps such as image recognition, natural language processing, fraud detection and forecasting. It is powered by Trainium, an Amazon-designed chip that the company claimed last year would offer the most teraflops in any machine learning instance in the cloud. (A teraflop translates into a chip capable of processing 1 trillion calculations per second.)

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