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What is Amazon SageMaker Model Monitor?

Amazon SageMaker Model Monitor allows users to select particular data for oversight and examination without requiring any coding skills. It offers a range of features, including the ability to monitor prediction outputs, while also gathering critical metadata such as timestamps, model identifiers, and endpoints, thereby simplifying the evaluation of model predictions in conjunction with this metadata. For scenarios involving a high volume of real-time predictions, users can specify a sampling rate that reflects a percentage of the overall traffic, with all captured data securely stored in a designated Amazon S3 bucket. Additionally, there is an option to encrypt this data and implement comprehensive security configurations, which include data retention policies and measures for access control to ensure that access remains secure. To further bolster analysis capabilities, Amazon SageMaker Model Monitor incorporates built-in statistical rules designed to detect data drift and evaluate model performance effectively. Users also have the ability to create custom rules and define specific thresholds for each rule, which provides a personalized monitoring experience that meets individual needs. With its extensive flexibility and robust security features, SageMaker Model Monitor stands out as an essential tool for preserving the integrity and effectiveness of machine learning models, making it invaluable for data-driven decision-making processes.

What is Amazon SageMaker Feature Store?

Amazon SageMaker Feature Store is a specialized, fully managed storage solution created to store, share, and manage essential features necessary for machine learning (ML) models. These features act as inputs for ML models during both the training and inference stages. For example, in a music recommendation system, pertinent features could include song ratings, listening duration, and listener demographic data. The capacity to reuse features across multiple teams is crucial, as the quality of these features plays a significant role in determining the precision of ML models. Additionally, aligning features used in offline batch training with those needed for real-time inference can present substantial difficulties. SageMaker Feature Store addresses this issue by providing a secure and integrated platform that supports feature use throughout the entire ML lifecycle. This functionality enables users to efficiently store, share, and manage features for both training and inference purposes, promoting the reuse of features across various ML projects. Moreover, it allows for the seamless integration of features from diverse data sources, including both streaming and batch inputs, such as application logs, service logs, clickstreams, and sensor data, thereby ensuring a thorough approach to feature collection. By streamlining these processes, the Feature Store enhances collaboration among data scientists and engineers, ultimately leading to more accurate and effective ML solutions.

Media

Media

Integrations Supported

Amazon SageMaker
Amazon Web Services (AWS)
AWS Glue
AWS Lake Formation
Amazon Athena
Amazon Kinesis
Amazon Redshift
Amazon S3
Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Apache Spark
Databricks Data Intelligence Platform
Snowflake

Integrations Supported

Amazon SageMaker
Amazon Web Services (AWS)
AWS Glue
AWS Lake Formation
Amazon Athena
Amazon Kinesis
Amazon Redshift
Amazon S3
Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Apache Spark
Databricks Data Intelligence Platform
Snowflake

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/sagemaker/model-monitor/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

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

Categories and Features

Machine Learning

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

Categories and Features

Machine Learning

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

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