AWS Offers Different Layers of Machine Learning
Saha noted that customers approach machine learning in different ways, so AWS seeks to meet them where they are in their implementation.
According to Saha, customers fall into one of three layers of development, and AWS offers services for each layer. “At the bottom layer are the machine learning infrastructure services. This is where we provide the machine learning hardware and software that customers can use to build their own machine learning infrastructure,” he said. “This is meant for customers with highly custom needs, and that is why they want to build their own machine learning infrastructure.”
Most organizations are in the middle layer, Saha explained. That involves AWS building the machine learning infrastructure so customers can focus on what Saha called “the differentiated work of building machine learning models.” In this layer, AWS customers can take advantage of its fully managed machine learning service, Amazon SageMaker.
At the top layer, AWS provides more than just the foundational ML infrastructure. These customers benefit from AI services “where AWS embeds machine learning into different use cases such as personalization, forecasting, anomaly detection, speech, transcription and others,” Saha said.
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Amazon SageMaker Is Democratizing Machine Learning
As Saha pointed out, most organizations using ML are somewhere in the middle layer of development, where Amazon SageMaker can help them reach their automation goals.
“Today, tens of thousands of customers of all sizes and across industries rely on Amazon SageMaker. AWS customers are building millions of models, training models with billions of parameters, and generating trillions of predictions every month. Many customers are using ML at a scale that was unheard of just a few years ago,” Saha said in a related press release.
“The new Amazon SageMaker capabilities announced today make it even easier for teams to expedite the end-to-end development and deployment of ML models. From purpose-built governance tools to a next-generation notebook experience and streamlined model testing to enhanced support for geospatial data, we are building on Amazon SageMaker’s success to help customers take advantage of ML at scale,” Saha said.