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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

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.

Description

Effortlessly create sophisticated predictive models without the need for mathematics or programming, all in just a few simple clicks. Our comprehensive solution takes you through the entire process, from data cleansing to model construction and prediction generation, ensuring you have everything you need. You can feel secure in your decisions, as we provide insights into the rationale behind AI-driven choices, empowering your business with actionable data insights. Experience the ease of data analytics within minutes, eliminating the necessity for coding. Our streamlined approach allows you to build machine learning algorithms, interpret results, and forecast outcomes with just a single click. Transition from raw data to valuable analytics seamlessly, without writing any code. Just upload your dataset, pose questions in everyday language, and receive the most effective model tailored to your data, which you can then easily share with others. Enhance customer productivity significantly, as we assist companies in harnessing no-code machine learning to elevate their customer experience and satisfaction levels. By simplifying the process, we enable organizations to focus on what truly matters—building strong relationships with their clients.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker
Amazon Web Services (AWS)

Integrations

Amazon SageMaker
Amazon Web Services (AWS)

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

2006

Country

United States

Website

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

Vendor Details

Company Name

ScoopML

Country

United States

Website

www.scoopml.app

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

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