AWS Machine Learning Blog
Author: Shreyas Subramanian
Shreyas Subramanian is a Principal data scientist and helps customers by using Machine Learning to solve their business challenges using the AWS platform. Shreyas has a background in large scale optimization and Machine Learning, and in use of Machine Learning and Reinforcement Learning for accelerating optimization tasks.
Running on-demand, serverless Apache Spark data processing jobs using Amazon SageMaker managed Spark containers and the Amazon SageMaker SDK
July 2023: This post was reviewed for accuracy. Apache Spark is a unified analytics engine for large scale, distributed data processing. Typically, businesses with Spark-based workloads on AWS use their own stack built on top of Amazon Elastic Compute Cloud (Amazon EC2), or Amazon EMR to run and scale Apache Spark, Hive, Presto, and other […]
Building a deep neural net–based surrogate function for global optimization using PyTorch on Amazon SageMaker
July 2023: This post was reviewed for accuracy. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. Customer X has the following problem: They are about to release a new car model to be designed for maximum fuel efficiency. In reality, thousands of […]
Bring your own hyperparameter optimization algorithm on Amazon SageMaker
July 2023: This post is outdated. We recommend referring to Amazon SageMaker Automatic Model Tuning now supports three new completion criteria for hyperparameter optimization for the latest solution. In this blog post, we’ll discuss how to implement custom, state-of-the-art hyperparameter optimization (HPO) algorithms to tune models on Amazon SageMaker. Amazon SageMaker includes a built-in HPO […]