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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Amazon EC2 Inf1 instances are specifically designed to provide efficient, high-performance machine learning inference at a competitive cost. They offer an impressive throughput that is up to 2.3 times greater and a cost that is up to 70% lower per inference compared to other EC2 offerings. Equipped with up to 16 AWS Inferentia chips—custom ML inference accelerators developed by AWS—these instances also incorporate 2nd generation Intel Xeon Scalable processors and boast networking bandwidth of up to 100 Gbps, making them suitable for large-scale machine learning applications. Inf1 instances are particularly well-suited for a variety of applications, including search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers have the advantage of deploying their ML models on Inf1 instances through the AWS Neuron SDK, which is compatible with widely-used ML frameworks such as TensorFlow, PyTorch, and Apache MXNet, enabling a smooth transition with minimal adjustments to existing code. This makes Inf1 instances not only powerful but also user-friendly for developers looking to optimize their machine learning workloads. The combination of advanced hardware and software support makes them a compelling choice for enterprises aiming to enhance their AI capabilities.

Description

Amazon SageMaker Feature Store serves as a comprehensive, fully managed repository specifically designed for the storage, sharing, and management of features utilized in machine learning (ML) models. Features represent the data inputs that are essential during both the training phase and inference process of ML models. For instance, in a music recommendation application, relevant features might encompass song ratings, listening times, and audience demographics. The importance of feature quality cannot be overstated, as it plays a vital role in achieving a model with high accuracy, and various teams often rely on these features repeatedly. Moreover, synchronizing features between offline batch training and real-time inference poses significant challenges. SageMaker Feature Store effectively addresses this issue by offering a secure and cohesive environment that supports feature utilization throughout the entire ML lifecycle. This platform enables users to store, share, and manage features for both training and inference, thereby facilitating their reuse across different ML applications. Additionally, it allows for the ingestion of features from a multitude of data sources, including both streaming and batch inputs such as application logs, service logs, clickstream data, and sensor readings, ensuring versatility and efficiency in feature management. Ultimately, SageMaker Feature Store enhances collaboration and improves model performance across various machine learning projects.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
AWS Deep Learning AMIs
AWS Inferentia
AWS Lake Formation
AWS Trainium
Amazon EC2
Amazon EC2 G5 Instances
Amazon EC2 P4 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon Elastic Block Store (EBS)
Amazon Elastic Container Service (Amazon ECS)
Amazon Redshift
Amazon S3
Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Apache Spark
Snowflake
TensorFlow

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
AWS Deep Learning AMIs
AWS Inferentia
AWS Lake Formation
AWS Trainium
Amazon EC2
Amazon EC2 G5 Instances
Amazon EC2 P4 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon Elastic Block Store (EBS)
Amazon Elastic Container Service (Amazon ECS)
Amazon Redshift
Amazon S3
Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Apache Spark
Snowflake
TensorFlow

Pricing Details

$0.228 per hour
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/ec2/instance-types/inf1/

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/feature-store/

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Alternatives

Alternatives

AWS Neuron Reviews

AWS Neuron

Amazon Web Services