When measuring overall use, AWS is the leader in delivering that technology, Jassy said, with twice as many customers using machine learning tools and frameworks on Amazon's cloud than any other provider, Jassy said.
Still, its early days for most customers, especially mainstream enterprises.
"All of them want to be using machine learning," he said.
But there just aren't that many machine learning experts in the world.
"We want everyday developers and scientists to be able to use machine learning much more extensively," he said.
To that end, Jassy introduced Amazon SageMaker, an "easy way to build, train and deploy machine learning models for everyday developers."
The service is modular, allowing users to build models, train algorithms, and host them in different environments.
Amazon's underlying machine learning technology has also been applied to powering a new set of audio and video capabilities.
One is AWS DeepLens, a wireless video camera integrated with deep learning hardware and tools. Developers can build computer-vision models for that device by leveraging SageMaker, and use the AWS GreenGrass IoT platform to set triggers on AWS Lambda serverless compute functions.
Jassy also introduced Amazon Batch Rekognition Video, which delivers real-time video recognition. Amazon Kinesis Video Streams improves secure ingestion and storage of those videos, or any time-encoded data stream.
Other new services are Amazon Transcribe for automatic speech recognition and Amazon Translate for artificially intelligent translation of text between languages. Those products can be used extensively to convert audio to text, then translate it into different languages.
But often those products produce reams of text that would take far too long for anyone to read. To that end, the new Amazon Comprehend service delivers fully-managed, natural language processing to cull through large stores of voice data converted to text.