In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions. The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready availability of virtually infinite compute capacity, […]
Amazon SageMaker goes for “Infinitely Scalable” Machine Learning
March 23, 2018 by
Over at the All Things Distributed blog, Werner Vogels writes that the new Amazon SageMaker is designed for building machine learning algorithms that can handle an infinite amount of data. “To handle unbounded amounts of data, our algorithms adopt a streaming computational model. In the streaming model, the algorithm only passes over the dataset one time and assumes a fixed-memory footprint. This memory restriction precludes basic operations like storing the data in memory, random access to individual records, shuffling the data, reading through the data several times, etc.”
