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Description
AWS IoT Core enables seamless connectivity between IoT devices and the AWS cloud, eliminating the need for server provisioning or management. Capable of accommodating billions of devices and handling trillions of messages, it ensures reliable and secure processing and routing of communications to AWS endpoints and other devices. This service empowers applications to continuously monitor and interact with all connected devices, maintaining functionality even during offline periods. Furthermore, AWS IoT Core simplifies the integration of various AWS and Amazon services, such as AWS Lambda, Amazon Kinesis, Amazon S3, Amazon SageMaker, Amazon DynamoDB, Amazon CloudWatch, AWS CloudTrail, Amazon QuickSight, and Alexa Voice Service, facilitating the development of IoT applications that collect, process, analyze, and respond to data from connected devices without the burden of infrastructure management. By utilizing AWS IoT Core, you can effortlessly connect an unlimited number of devices to the cloud and facilitate communication among them, streamlining your IoT solutions. This capability significantly enhances the efficiency and scalability of your IoT initiatives.
Description
Amazon SageMaker Model Monitor enables users to choose which data to observe and assess without any coding requirements. It provides a selection of data types, including prediction outputs, while also capturing relevant metadata such as timestamps, model identifiers, and endpoints, allowing for comprehensive analysis of model predictions in relation to this metadata. Users can adjust the data capture sampling rate as a percentage of total traffic, particularly beneficial for high-volume real-time predictions, with all captured data securely stored in their designated Amazon S3 bucket. Additionally, the data can be encrypted, and users have the ability to set up fine-grained security measures, establish data retention guidelines, and implement access control protocols to ensure secure data handling. Amazon SageMaker Model Monitor also includes built-in analytical capabilities, utilizing statistical rules to identify shifts in data and variations in model performance. Moreover, users have the flexibility to create custom rules and define specific thresholds for each of those rules, enhancing the monitoring process further. This level of customization allows for a tailored monitoring experience that can adapt to varying project requirements and objectives.
API Access
Has API
API Access
Has API
Integrations
Amazon SageMaker
Amazon Web Services (AWS)
AWS App Mesh
AWS CloudTrail
AWS IoT
AWS IoT ExpressLink
AWS Lambda
Amazon CloudWatch
Amazon DynamoDB
Amazon Kinesis
Integrations
Amazon SageMaker
Amazon Web Services (AWS)
AWS App Mesh
AWS CloudTrail
AWS IoT
AWS IoT ExpressLink
AWS Lambda
Amazon CloudWatch
Amazon DynamoDB
Amazon Kinesis
Pricing Details
No price information available.
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/iot-core/
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/model-monitor/
Product Features
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization
IoT Analytics
Activity Dashboard
Activity Tracking
Analytics
Asset Tracking
Data Collection
Data Synchronization
Data Visualization
ETL
Multiple Data Sources
Performance Analysis
Real-Time Analytics
Real-Time Data
Real-Time Monitoring
Status Tracking
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization