Machine Design
Swami Sivasubramanian

Special Delivery: Machine Learning Tips from Amazon Web Services

June 29, 2020
For the uninitiated, deploying real-world machine learning can pose obstacles. Amazon AI’s lead has tips on avoiding pitfalls.

Every year, during Amazon’s annual planning process, leaders in every business unit are asked a pointed question: How do you plan to leverage machine learning in your business?

The words “we don’t plan to” are not an acceptable answer, said Swami Sivasubramanian, vice president of Amazon AI at Amazon Web Services. 

Speaking from a virtual stage at the Collision from Home conference, Sivasubramanian told the audience that the world has already entered the golden age of artificial intelligence and machine learning.

The online conference was orchestrated by the producers of the world’s largest tech conference, Web Summit, and boasted more than 30,000 attendees (June 23-25). It was arguably the ideal platform for a cloud-based, live meet-up to discuss long-term trends in the digital world and for Sivasubramanian’s talk, “No hype: Deploying real-world machine learning.”

ML has seen traction across industries and supply chains, and there are no shortages of examples of CIOs and CEOs professing how AI and ML are transforming business for the better, said Sivasubramanian. His list of illustrations included the financial sector, where Intuit uses machine learning to forecast their contact center volume as a way to staff up; the medical field, where Aidoc leverages AI and computer vision to build systems that assist radiologists with image scans; and the public sector, where agencies such as NASA use ML algorithms to explore extreme conditions associated with superstorms. (NASA partners with AWS to detect solar flares based on signal anomalies that occur in space.)

Amazon’s own journey goes back more than 20 years, when the digital conglomerate (today worth $1.3 trillion) started using machine learning technology for its supply chain, fulfillment centers and last-mile delivery. Amazon’s Echo voice-controlled smart speaker device is just a recent example of a product with development roots inside Amazon Lab126 dating back 10 years.

Back then, Jeff Bezos and his leadership team began to realize that machine learning was about to go through a pivotal moment. “With the advent of new technologies, such as deep learning on the horizon, they started realizing that every line of business is going to need to have a machine learning strategy,” said Sivasubramanian.

Since then, explained Sivasubramanian, every line of business at Amazon—irrespective of whether they are running technology, research, human resources, finance or supply chain—is asked to consider how it can enhance the customer experience using machine learning in a meaningful way. Along the way Amazon learned a few lessons about applying ML successfully, all of which Sivasubramanian demarcates as four pitfalls.
  1. Get data in order.
  2. Understand where to apply machine learning.
  3. Address the skills gap.
  4. Don’t do the undifferentiated heavy lifting.

Four Machine Learning Pitfalls

At Amazon, Sivasubramanian’s professional repertoire extends to bootstrapping the NoSQL database ecosystem, as well as the AWS stack: ML algorithms (deep learning frameworks and ML algorithms); ML platform services; and AI application services such as Lex (rich conversational experiences), Polly (text to speech service) and Rekognition (image processing service). Culled from his online presentation and edited for clarification, the following tactical pointers can be applied to avoid obstacles.

1. Get Data in Order

When data scientists are asked to name the biggest impediment when it comes to machine learning, they say “data,” asserts Sivasubramanian. “More than 50% of data scientists spend their time in data wrangling, annotation, ETL and so forth,” he said, noting that the way to avoid this and accelerate machine learning, is to ask three questions: What data is available today? How can it be made easily available so that you can get started? And in a year’s time, what data will we wish we had so that we can start collecting today, and so that we continue to build a durable advantage for years to come?

2. Understand Where to Apply Machine Learning

Picking the right business problem is important, said Sivasubramanian, who compartmentalizes them along three dimensions: data readiness, business impact and machine learning applicability. “Machine learning algorithms and research have come a long way in solving the problems,” he said. “If you pick a problem where the data is not ready and machine learning research hasn’t been developed enough to solve this problem, but it is high business impact, you can throw a lot of resources at it.

“But if you force a deadline, it’s going to lead to frustrated data scientists. On the other hand, if you pick a low-business-impact problem but high data and machine learning applicability, it could be a good prototype to build experience. Ideally, what you want is a problem that scores high on these three dimensions because it’s a great place to start.”

Sivasubramanian advised against building a group of technical experts in machine learning and placing them in a separate team without any contact with domain experts. “What typically happens is that technical experts tend to build interesting proof-of-concepts with no take off from business,” said Sivasubramanian, adding that the ideal team comprised of domain experts and technical experts will “work backwards from the customer and build something meaningful.”

3. Address the Skills Gap

There are not enough people who know machine learning, said Sivasubramanian. He pointed to World Economic Forum data, which shows that jobs such as artificial intelligence and machine learning specialists or data scientists are forecasted to be among the most in-demand roles across most industries by 2022. Amazon started addressing this demand through its Machine Learning University about six years ago when it started training engineers and product managers, said Sivasubramanian.

4. Don’t do the Undifferentiated Heavy Lifting

The final pitfall, according to Sivasubramanian, is that organizations become excited about solving undifferentiated heavy lifting and, by extension, fall prey to the idea of building a machine learning platform, a translation engine or a contributors’ engine.

In a 2006 speech, “We Build Muck, So You Don’t Have To,” Jeff Bezos defined “undifferentiated heavy lifting” as server hosting, bandwidth management, contract negotiation, scaling and managing physical growth, as well as dealing with the accumulated complexity of heterogeneous hardware and co-ordinating large teams to manage each of these areas.

Sivasubramanian offers this advice: “What you ideally want is your engineers to focus on things that matter to the business and leverage things from clouds, such as AWS, or from open-source technologies, and solve the purely differentiated business problem.”

Avoiding these pitfalls will set enterprises up for a future of machine learning where “businesses move from being reactive to proactive, to automate their processes from manual to automated processing, and from generalized customer experience to personalized experiences, and to taking technology from being obscure to being accessible,” he said.

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