3 use cases for machine learning you probably haven’t thought of

BrandPost By Keith Shaw
Aug 17, 2020
AnalyticsMachine Learning

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Credit: iStock

As organizations gain more experience deploying machine learning (ML) and artificial intelligence (AI) across different parts of the business, they’re discovering new and interesting ways to use the technology.

Typical use cases include established applications such as personalization, fraud detection, and speech recognition. But there’s much more to explore.

“The cloud enables extremely low-cost compute and storage, which opens up opportunities for more modeling,” says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab. “There’s lots of innovation yet to happen. We are barely scratching the surface.”

Here are three examples of how machine learning can extend beyond traditional use cases to drive positive business outcomes.

#1 Computer vision

Computer vision lets machines identify people, places or objects with accuracy above human levels. Combined with ML models, computer vision can be deployed across many different areas of a business, from identifying defects in a high-speed assembly line to automating content management.

TurboTax creator Intuit has leveraged computer vision and machine learning, to help users file their taxes more efficiently. Integrating computer vision with Amazon Textract, the solution lets users scan pictures of their tax forms, including W2s and 1099s. The service then verifies accuracy to identify any missing data or anomalies, using contextual data from an existing database of tax codes and compliance forms.

Amazon also has pioneered the use of computer vision across its operations. For example, the robotics group at fulfillment centers uses machine vision and sensor fusion to route packages faster. Amazon Go stores, where customers can buy items without waiting in line for a cashier, utilize both computer vision and ML-based predictive capabilities.

#2 Predicting behaviors

 Organizations can apply ML models to customer data to anticipate future behavior, which can lead to innovative services.

For example, Domino’s Pizza Enterprises Limited, a Domino’s franchise holder with brands in Australia and Europe, created a predictive ordering solution to help stores anticipate what pizza their customers would order. This capability is part of a broader initiative to reduce pickup and delivery times: Pizzas can be ready for pickup in as little as three minutes or delivered within 10 minutes.

Separately, online retailer Zappos uses analytics and machine learning to help provide personalized sizing and search results for customers, as well as predictive behavior models. Using Amazon SageMaker, Zappos created models to predict customer apparel sizes, which are cached and exposed at runtime via microservices for use in recommendations. The system enabled single-digit millisecond response times and can handle more than 10 trillion requests per day.

By using the search result speed with prediction models, Zappos reduced repeat searches and product returns and achieved higher search-to-product clickthrough rates.

#3 Sustainability and the environment

Organizations have recognized that by applying ML models to the data they have on business processes, they can achieve new insights that help reduce waste and preserve natural resources.

For example, startup Saildrone used ML to help complete environmental projects such as quantifying the behavior and trends of fish stocks and their predators, including sharks and seals, for better and sustainable fishery management.

The company also used ML to help autonomous sailing drones to circumnavigate Antarctica, giving researchers key insights into ocean and climate processes.

“It had never been done before … you would need to understand the risk of collision with icebergs, which can be the size of small countries,” Sebastien de Halleux, chief operating officer at Saildrone, said in a recent video. “AWS was a natural partner in trying to get a quick start around both the storage and computer infrastructure in a way that can scale, literally, planetwide.”

These are just a few examples of the many new ways organizations can apply ML to address different challenges.

“What really excites me,” says Elaprolu, “is using ML to solve real-world problems and seeing the benefits customers can get.”

Learn more about ways to reinvent your business with data.

For more machine learning insights from Sri Elaprolu, check out the new Ahead of the Pack podcast.