Amazon recently launches new AI services to help boost the businesses using business intelligence applications. These services are also helpful in DevOps. Let’s have a look at some of the services that are being released by Amazon and also some of the services that are going to be launched.
SageMaker Data Wrangler was introduced by Amazon today, a new AWS service designed to speed up data formation for AI and machine learning applications. In addition, SageMaker Feature Store, a purpose-built product for naming, arranging, locating, and sharing features, or the individual independent variables that serve as inputs in a machine learning system, was removed from the wraps. In addition, Amazon unveiled SageMaker Pipelines, which was identified as a CI/CD service for AI by CEO Andy Jassy. And the company detailed DevOps Guru and QuickSight Q, offerings that use machine learning to detect organizational challenges, provide market intelligence, and find answers to questions in information stores, as well as new items on the business side of Amazon's contact center and industrial sides.
Jassy said during a keynote at Amazon's reinvention conference that Data Wrangler has over 300 forms of conversion transformation built-in. In a target dataset, the service proposes data-based transformations and applies these transformations to functions, offering a real-time overview of the transformations. As for the SageMaker Feature Store, Jassy said that the service, which is accessible from SageMaker Studio, functions as a feature storage component and can access features in either subsets or batches. Data Wrangler also checks to ensure that the information is' true and balanced.' Meanwhile, SageMaker Pipelines allows users to identify, share, and reuse every step of an end-to-end machine learning workflow with customizable workflow templates that are preconfigured while documenting each step in SageMaker Experiments.
The DevOps Guru is a totally different beast. Amazon claims that when it is deployed in a cloud environment, missing or misconfigured warnings can be detected to warn about approaching resource limits and changes in code and configuration that could trigger outages. Furthermore, DevOps Guru highlights problems like under-provisioned processing power, overuse of database I/O, and memory leaks while suggesting steps to be remedied.
The aim of Amazon QuickSight, which was already widely available, is to provide scalable, embeddable cloud-tailored business intelligence solutions. To that end, without any infrastructure maintenance or capacity planning, Amazon claims it can scale up to tens of thousands of customers. QuickSight can be integrated into dashboard applications and is available with pricing like pay-per-session, producing dashboard summaries automatically in simple language. A new complimentary service called QuickSight Q addresses natural language questions, drawing on tools available and using natural language processing to understand domain-specific business language and produce responses that represent the jargon of the industry.
A few days ago, Amazon didn't miss the chance to roll out updates through its omnichannel cloud contact center offering, Amazon Connect. Real-Time Contact Lens, which detects problems in real-time to influence consumer behavior during calls, is new as of today. Amazon Connect Voice ID, which often operates in real-time, performs authentication "without interrupting natural conversation" using machine learning-powered voice analysis. And Connect Tasks ostensibly makes follow-up tasks simpler for agents by allowing managers to completely automate those tasks.
Amazon has also released new Amazon Service Monitron, an end-to-end monitoring device for predictive sensor maintenance, a gateway, an instance of AWS cloud, and a smartphone app. Amazon Lookout for Equipment, an adjacent service, sends sensor information to AWS to construct a machine learning model, pulling data from machine operating systems such as OSIsoft to learn normal patterns, and to detect early warning signs that could lead to machine failures using real-time data.
AWS Panorama Appliance
There's the AWS Panorama Appliance, a new plug-in appliance from Amazon that links to a network and recognizes video streams from existing cameras, for industrial businesses searching for a more holistic, computer vision-centric analytics solution. The Panorama Appliance provides manufacturing, retail, construction, and other industries with computer vision models support SageMaker-built models and integrate with AWS IoT services, including SiteWise, to submit data for broader study.
AWS Panorama SDK
The AWS Panorama SDK allows hardware vendors to create new cameras that run computer vision at the edge in shipping alongside the Panorama Appliance. That is built to work with Nvidia and Ambarella chips which are designed for computer vision and deep learning. Amazon says Panorama-compatible cameras are going to work with AWS machine learning services out of the box. In SageMaker, clients can create and train models and deploy them on cameras with a single click.
AWS Trainium, a chip custom-designed to offer what the company identifies as cost-effective machine learning model training in the cloud, the slew of announcements arrive right after Amazon unveiled it. Amazon reports that Trainium can deliver the most teraflops of any machine learning instance in the cloud, where a teraflop translates to a chip that can process 1 trillion calculations a second when it becomes available in 2021.
Ready to explore Amazon's cutting-edge AI services for DevOps and Business Intelligence applications? Dive into the latest advancements, from SageMaker's powerful Data Wrangler to the game-changing DevOps Guru. For tailored insights and assistance, Reach out to us.