In an episode of HBO’s “Silicon Valley” this year, two of the characters build a startup company that offers image recognition technology to distinguish what is truly a hot dog and what is not. Clips of the segment spread like wildfire around Silicon Valley, and several enterprising developers actually built real hot dog recognition apps. This example provides humorous insight into what could be possible if machine learning tools were more available and easier to use.

Now, Amazon Web Services Inc. has turned satire into reality with DeepLens, a $250 artificial intelligence-infused camera that can quickly recognize a wide array of everyday objects. Even hot dogs.

“We don’t build technology because it’s cool. We build technology because it’s what our customers want,” said Swami Sivasubramanian (pictured), vice president of artificial intelligence at AWS.

Sivasubramanian visited the set of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, and spoke with co-hosts John Furrier (@furrier) and Stu Miniman (@stu) during the AWS re:Invent event in Las Vegas. They discussed new tools offered by AWS to spread machine learning among developers and examples of how the technology is being applied.

New tools make deployment easier

The introduction of DeepLens at AWS re:Invent was accompanied by the release of SageMaker, a new platform for creating and deploying machine learning algorithms. Machine learning is a key part of the AWS solutions base as the company seeks to follow the same path it took in building a multifaceted portfolio of services for its cloud customers.

Although Amazon had been using machine learning for 20 years, with applications for its product recommendations engine and robotic fulfillment, the company had grown concerned about the length of time it took developers to use existing tools such as Tensorflow, according to Sivasubramanian.

“[Machine learning] should not be this hard, so we wanted to do what AWS did to the IT industry for compute, storage and databases,” Sivasubramanian said. “We wanted to do the same for machine learning by making it really easy to get started and consume it as a utility.”

As machine learning tools become more widely available, the use cases are beginning to grow. One nonprofit — Marinus Analytics LLC — is using the Amazon facial matching application Rekognition to identify potential criminals in human trafficking.

“We’re seeing some amazing [examples] of how developers are using machine learning,” Sivasubramanian said. “We have only just scratched the surface. There is so much stuff to do.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent.

Photo: SiliconANGLE