AWS Neuron

AWS Neuron

Amazon Web Services
Feast

Feast

Tecton
+
+

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About

It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP).

About

Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Organizations in need of an SDK solution with a compiler, runtime, and profiling tools that unlocks high-performance and cost-effective deep learning acceleration

Audience

Anyone looking for an open-source feature store which provides easy access to consistent features across model training and online inference

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Amazon Web Services
Founded: 2006
United States
aws.amazon.com/machine-learning/neuron/

Company Information

Tecton
Founded: 2019
United States
feast.dev/

Alternatives

Alternatives

JFrog ML

JFrog ML

JFrog

Categories

Categories

Integrations

AWS Deep Learning AMIs
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn2 Instances
Amazon EKS
Amazon EKS Anywhere
Amazon EMR
Amazon ElastiCache
Amazon Redshift
Amazon S3
Amazon SageMaker
Amazon Web Services (AWS)
DataHub
Delta Lake
Python
Snowflake
TensorFlow
ZenML

Integrations

AWS Deep Learning AMIs
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn2 Instances
Amazon EKS
Amazon EKS Anywhere
Amazon EMR
Amazon ElastiCache
Amazon Redshift
Amazon S3
Amazon SageMaker
Amazon Web Services (AWS)
DataHub
Delta Lake
Python
Snowflake
TensorFlow
ZenML
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Claim AWS Neuron and update features and information
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