Startups across AWS Accelerators use AI and ML to solve mission-critical customer challenges

Relentless advancement in technology is improving the decision-making capacity of humans and enterprises alike. Digitization of the physical world has accelerated the three dimensions of data: velocity, variety, and volume. This has made information more widely available than before, allowing for advancements in problem-solving. Now, with cloud-enabled democratized availability, technologies

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Shared by AWS Machine Learning November 1, 2022

Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. It’s an automated machine learning (AutoML) solution that eliminates the heavy lifting of handwritten ML models that requires ML expertise. Data scientists need to only provide a tabular dataset and select

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Shared by AWS Machine Learning November 1, 2022

Exploring the business side of AI

Transcript from October 11th Deep Dive: AI Business panel Stefano Maffulli: Then welcome everyone officially…. The post Exploring the business side of AI first appeared on Voices of Open Source. Click Here to View Original Source (opensource.org)

Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

This post is co-written with Chaim Rand from Mobileye. Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. When both types of

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Shared by AWS Machine Learning October 27, 2022