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AWS re:Invent: Intelligent services, closer clouds

A long, long time ago, Amazon Web Services was all about hosting computing workloads in data centers far, far away.

At its AWS re:Invent 2019 event, the company acknowledged that computing power can be more useful when it’s closer to home, announcing three new services for reducing latency, including a mini-cloud you can house in your own data center.

The company also released a raft of new AI services, including ways for enterprises to leverage machine learning without prior experience and a service that aims to help customers pick the most appropriate AWS compute services for their needs.

Quantum computers may one day solve intractable optimization problems beyond the scope of today’s computers, but the conditions necessary to cool a quantum processor to its operating temperature of -273C (-460°F) suggest quantum computing will likely be beyond the reach of most data centers.

When Amazon Braket becomes generally available, AWS will charge by the hour for services including access to its design tools, testing algorithms on simulators, running jobs on a real quantum computer, or training machine-learning algorithms using quantum technologies.

AWS Outposts are the most obvious approach: These fully managed compute and storage racks are built with the same hardware Amazon uses in its datacenters, run the same workloads as AWS compute and storage instances, and are managed with the same tools and the same APIs.

This kind of dedicated capacity will cost from around $7,000 per month or $225,000 up front for the smallest system, or four times that for a memory-optimized unit designed for running large on-premises databases.

If you operate in an industry where you constantly need to exchange high volumes of data with other companies with latencies under 10 milliseconds — video editing and post-production, for example — then you’re probably already located close to these companies, but you may not be close to an AWS data center.

It’s initially targeting mobile applications like game streaming and augmented or virtual reality services for consumers but could also serve more exacting users such as autonomous vehicles and industrial robots.

SageMaker Model Monitor follows machine learning systems after training, warning when they’re asked to work outside their design parameters, and SageMaker Autopilot analyzes raw data, selects the learning algorithms it considers most appropriate, then trains a selection of models with different trade-offs.

The idea is that employees will use natural language to pose questions such as “When does the helpdesk open?” and Kendra will comb through unstructured text from multiple applications, searching documents the employee is authorized to access to find the answer.

Amazon CodeGuru plugs into source code repositories such as GitHub or CodeCommit, responding to pull requests by evaluating code changes for quality and recommending how to remediate any flaws it finds just as a human code reviewer would.

Machine learning models don’t always produce clear-cut answers to challenges such as image recognition or content moderation, requiring developers to code the sending of lower-confidence responses to a human for review.

Amazon's AI Leadership Advances at re:Invent 2019

Amazon Web Services (AWS) was true to form last week as over 65,000 customers, partners and analysts descended on Las Vegas for re:Invent 2019.

CEO Andy Jassy’s signature three-hour marathon keynote was once more a whirlwind of customer case studies and digs at the competition backed by an '80s-fueled house band.Van Halen, Billy Joel and other blasts from the past provided the soundtrack to this year's core theme of business transformation and how companies can get value out of the AWS cloud.

The NFL club announced it had selected AWS as its preferred cloud vendor in advance of the show, stating its AI is enabling better player tracking, performance analytics and video analysis.

According to my firm, CCS Insight's annual survey of IT decision-makers this fall, 57% of US and European businesses that are deploying AI said they favor either a single cloud or a preferred cloud strategy when it comes to their data and machine learning requirements.

SageMaker Studio includes the new SageMaker Debugger, a fully managed debugging service for the real-time monitoring of models that warns and provides remediation advice when issues are detected and SageMaker Model Monitor, a service which detects concept drift in models and alerts developers when the performance of a model running in production begins to deviate from the original trained model.

Vueling’s head of data and analytics told me that the advancements will enable their vision of having, in the future, more machine learning models used by the business units and governed by their data center of excellence.

As the market shifts from experimentation to the operationalization of ML into business processes, SageMaker is evolving quickly to meet this shift, especially in the areas of machine learning lifecycle management, explainability and governance, which are hot areas now.

One of the most intriguing announcements of all was Amazon Kendra, a new enterprise search offering which uses natural language processing to make information searching easier through connectors to data stored in SharePoint online, JDBC and Amazon S3 repositories.

Search is an area customers frequently list as being broken in their organizations and since AWS doesn’t have a wide range of SaaS applications which generate a corpus of information that its AI can improve for search, it is an interesting move and part of its strategy in helping firms customize AI to specific industry and business challenges.

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