AWS Machine Learning Blog
Author: Shreyas Subramanian
Shreyas Subramanian is a AI/ML specialist Solutions Architect, 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.
Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS
Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units (vCPUs and GPUs) to train. This […]
Intelligently search your Jira projects with Amazon Kendra Jira cloud connector
Organizations use agile project management platforms such as Atlassian Jira to enable teams to collaborate to plan, track, and ship deliverables. Jira captures organizational knowledge about the workings of the deliverables in the issues and comments logged during project implementation. However, making this knowledge easily and securely available to users is challenging due to it […]
Automatically detect sports highlights in video with Amazon SageMaker
Extracting highlights from a video is a time-consuming and complex process. In this post, we provide a new take on instant replay for sporting events using a machine learning (ML) solution for automatically creating video highlights from original video content. Video highlights are then available for download so that users can continue to view them […]
Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language
You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), TensorFlow Extended (TFX), Argo, Luigi, and Amazon SageMaker Pipelines. All these tools help you compose pipelines in various languages (JSON, YAML, Python, and more), followed […]
Build reusable, serverless inference functions for your Amazon SageMaker models using AWS Lambda layers and containers
December 1, 2021: Amazon SageMaker Serverless Inference (preview) is a new inference option that enables you to easily deploy machine learning models for inference without having to configure or manage the underlying infrastructure. Watch for upcoming further details and check out the official announcement to learn more! In AWS, you can host a trained model […]
Solving numerical optimization problems like scheduling, routing, and allocation with Amazon SageMaker Processing
In this post, we discuss solving numerical optimization problems using the very flexible Amazon SageMaker Processing API. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. This pattern is relevant to solving business-critical problems such as scheduling, routing, allocation, shape optimization, trajectory optimization, […]
Running on-demand, serverless Apache Spark data processing jobs using Amazon SageMaker managed Spark containers and the Amazon SageMaker SDK
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 big data frameworks. This is useful for persistent […]
Building a deep neural net–based surrogate function for global optimization using PyTorch on Amazon SageMaker
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 parameters that represent tuning parameters relating to the […]
Bring your own hyperparameter optimization algorithm on Amazon SageMaker
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 algorithm, but provides the flexibility to use your own HPO algorithm. We’ll provide you with a framework to incorporate an HPO algorithm that you choose. However, before we […]