AWS Courts New Audiences as AI Headlines re:Invent 2020
AWS re:Invent 2020, held as a virtual event this year for more than 500,000 attendees, was once again a blitzkrieg of major announcements that collectively have the potential to reshape the course of enterprise technology over the next few years.
Among the big news in several areas of AWS's cloud portfolio, artificial intelligence (AI) and machine learning took top billing, with the company actively courting new, more business-oriented audience for its solutions this year.
Below, I take a look at some of the highlights and assess what they mean for the market and Amazon's strategy, which is starting to move into some important new directions.
AI Goes Mainstream
AWS CEO Andy Jassy kicked off the event, highlighting how AI is shifting from a niche experiment inside technical departments to becoming more mainstream in business processes, a trend my firm, CCS Insight, has also observed. For example, more than 80% of companies in our Senior Leadership IT Investment Survey fielded in July 2020, are now trialing AI or have put it into production, up considerably from the 55% reported in 2019.
According to AWS, "tens of thousands" of customers are now standardizing on Amazon SageMaker, its fully managed platform to build, train and deploy machine learning models, with "hundreds of thousands" accessing its AI services, such as Amazon Polly, Rekognition and Lex, in the latter's case, twice as many as any other cloud provider according to the firm. This includes customer Intuit, which was highlighted in the keynote as a firm that is reinventing its culture with AI, as the number of machine learning models it has deployed has shot up by 50% in the past year.
AWS breaks down its capabilities into three domains: frameworks and infrastructure; SageMaker; and AI services — its suite of off the shelf models, developer APIs and business solutions. Reinforcing its mission to "put machine learning into the hands of every developer and business," announcements covered all three of these domains this year, with a particular focus on SageMaker and higher-level, applied AI solutions in areas like business operations, contact centers and industrial and healthcare verticals.
Related Article: Amazon's AI Leadership Advances at re:Ignite 2019
SageMaker Gets a Revamp, Including Tackling Bias in Data
We have previously argued that Amazon SageMaker has become one of AWS's most important products, and re:Invent 2020 saw several new additions to the platform. These including SageMaker Data Wrangler, which provides tools to simplify the preparation of data, and SageMaker Feature Store, a fully managed repository within SageMaker Studio for the sharing of machine learning features — attributes in models used to streamline training.
Additionally, SageMaker Pipelines is an MLOps capability that provides a continuous integration and continuous delivery service, purpose-built for machine learning, and SageMaker Edge Manager helps customers operate machine learning models across fleets of edge devices up to 25 times faster.
But the most important improvement by far was Amazon SageMaker Clarify, which helps companies tackle bias in machine learning using tabular and statistical data. The service evaluates both training and inference data for bias by providing several statistical metrics about the data. It also monitors the performance of models in production against bias by checking how they perform against the training data.
Clarify also tackles model explainability using libraries of Shapley additive explanations (a set of open source tools that incorporate methods to explain ML outputs such as predictions) hardened in the platform. By combining several of its products, AWS is integrating explanations into several areas of the machine learning lifecycle from pre-training, to model analysis in training, to production and deployment.
Detecting bias in data and model behavior, and tackling black box AI with greater system transparency through explanations are among the most important requirements we're now seeing in the market, and while solutions are still quite immature, it's great to see AWS really homing in on this. Although it's a latecomer to this area, Clarify will be one of the most welcome improvements to SageMaker and a boost for customers who want more in the field of responsible AI.
Moving Up the Stack: Applied AI for Businesses and Industries
Another important area has been AWS's continued expansion up the stack into higher-level services and solutions for businesses and vertical markets. This year, the company concentrated on three main fields for these solutions: business operations, business intelligence and contact centers.
In business operations, it announced a preview of Amazon DevOps Guru, a fully managed service for software development that uses machine learning to help developers automatically detect operational problems and recommend fixes as part of their processes. It also launched Amazon Lookout for Metrics, an anomaly detection service offering root cause analysis and recommendations for operational time series data.
In business intelligence, and as an extension of its Amazon QuickSight service, it unveiled Amazon QuickSight Q, which uses natural language processing to enable businesses to ask questions about their structured data in everyday language. It also announced the integration of Autopilot, its automated machine learning solution, into Amazon Redshift and Neptune database products. This helps database engineers lacking machine learning experience to build and deploy models directly in those environments.
Lastly, there was a large set of announcements for Amazon Connect, its contact center product. They included among others, Amazon Connect Wisdom, a service that taps machine learning to enable contact center agents to search their various corporate knowledge bases for relevant content when handling calls. The shift to remote operations during the pandemic has helped transform contact centers around the globe, and this has been a major boon for Connect. The platform signed on 5,000 new customers this year alone and is becoming an important showpiece for AWS's AI services and growing portfolio of SaaS products.
Related Article: AI for Customer Experience: Late Adopters Are Reaping the Benefits
AWS Ups the Ante with Vertical Market and Industrial AI Solutions
The areas grabbing the biggest headlines, however, were in solutions for vertical markets, particularly for industries hard-hit by the pandemic, such as healthcare and industrial sectors.
Building on its Amazon Comprehend and Transcribe Medical services launched in 2019, AWS announced Amazon HealthLake, a new, HIPAA-eligible cloud-based service that applies machine learning to large volumes of health and life sciences data.
It also released several products for industrial sectors aimed at improving assembly line production, quality management, worker safety and remote operations in factories and warehouses.
- Amazon Monitron is an end-to-end machine monitoring system that employs machine learning to enable predictive monitoring of industrial machinery such as bearings, motors, pumps and conveyer belts. Comprising sensors, a gateway device and a mobile app, the system can be deployed in as little as an hour, analyzing vibration and temperature data to identify potential failures or abnormal activity.
- Amazon Lookout for Equipment allows businesses to harness their existing internet of things sensors to detect abnormal behavior through machine learning, which is also used in Amazon Lookout for Vision to quickly analyze large volumes of images and spot defects or irregularities.
- AWS Panorama, an appliance and software development kit, enables businesses to enhance on-premises cameras with computer vision, allowing them to analyze and make AI-based predictions about the content of a video stream locally.
The moves are vitally important because they signal several important changes in its strategy. Firstly, the firm is clearly putting its foot on the accelerator in the race to become the best cloud for industrial workloads and the transition to Industry 4.0. It's also getting better at utilizing the capabilities of Amazon.com as well, especially in the areas of fulfillment and factory operations technology, but also in its Prime Air and Amazon Go businesses as well. If there's one tech firm held as the gold standard for industrial operational efficiency with new technology, that company is Amazon. And while its retail arm is not without controversy, more solutions in these areas could become formidable assets as it competes more deeply in industries against Microsoft, IBM and Google in the future.
But above all, they show us that AWS is starting to reach new audiences beyond its core technical developer and data scientist communities and into more business-focused communities such as C-suite executives, business intelligence professionals, operations teams, business analysts and database engineers for example. For machine learning to reach its potential in the enterprise market, it needs to be far more pervasive with business users who have little to no expertise with the technology. It is this gap that many of these solutions are starting to bridge.
Related Article: Choosing the Right AI for Your Business Goals
What it All Means
What's striking this year is that AWS is not only assembling a market-leading AI portfolio, especially in higher-level services, but in doubling-down on business and industry problems, it's also becoming much more purposeful with new products and in reaching new audiences for its solutions.
At the same time, by tackling tricky areas with Amazon SageMaker such as data bias and explainability, MLOps, feature reuse and data preparation, it continues to address the immediate pains machine learning practitioners have with the technology.
With an eye-watering 250 new machine learning capabilities in the past 12 months alone, few are innovating faster at the moment. For a glimpse of this progress, take a look at how its portfolio has expanded in just two years from the below image I took at the event a couple of years ago and compare with the one from this year.
New Directions for AWS
While re:Invent 2020 revealed a step change and new directions in AWS's strategy, the firm isn't without challenges as it looks ahead. Its pace can be bewildering at times and the firm will need to simplify its portfolio and continue to focus on integrating its endless array of new features. This is particularly true for SageMaker, where the vast number of product names can often blur important distinctions between simple features and highly strategic services. A good example is the need to integrate the platform with AWS Outposts, a key solution that, in my opinion, will likely arrive soon for the growing number of its customers that want to run machine learning on their premises.
Above all, AWS will also need to grow more confident in not only responding to customer needs as the primary focus of its product strategy, but also anticipating their demands by offering more forward-looking products, and, based on its experience with AI internally, helping with implementation practices. Several emerging areas that went under the radar this year, such as security and privacy, are vital themes in the context of AI for customers at the moment. They will need to come into deeper focus in the near future to help firms build trust in AI, particularly with business decision makers. According to our recent survey of C-Suite executives, for example, security and privacy for machine learning are now priorities for business leaders focusing on AI strategy, and more than 80% of respondents are concerned about ethical perils stemming from the uses of AI.
AI certainly grabbed many of re:Invent’s headlines this year. But deeper focus on these areas will be vital for AWS to maintain its dominant position in 2021 and beyond.
About the Author
Nicholas McQuire is vice president, enterprise research and artificial intelligence research at CCS Insight. He has over 15 years' experience in enterprise technology advisory services. He leads CCS Insight research in cloud computing, machine learning and the digital workplace.
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