Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
With Amazon SageMaker Pipelines, you can effortlessly develop machine learning workflows using a user-friendly Python SDK, while also managing and visualizing your workflows in Amazon SageMaker Studio. By reusing and storing the steps you create within SageMaker Pipelines, you can enhance efficiency and accelerate scaling. Furthermore, built-in templates allow for rapid initiation, enabling you to build, test, register, and deploy models swiftly, thereby facilitating a CI/CD approach in your machine learning setup. Many users manage numerous workflows, often with various versions of the same model. The SageMaker Pipelines model registry provides a centralized repository to monitor these versions, simplifying the selection of the ideal model for deployment according to your organizational needs. Additionally, SageMaker Studio offers features to explore and discover models, and you can also access them via the SageMaker Python SDK, ensuring versatility in model management. This integration fosters a streamlined process for iterating on models and experimenting with new techniques, ultimately driving innovation in your machine learning projects.
Description
SageVue provides instant visual insights into the status of your system's health. For those seeking detailed analysis, granular options are available as well. It enables you to navigate through subnets effortlessly, allowing management of all your systems from a unified administrative console. With SageVue, you have the ability to perform remote firmware updates, whether on individual devices or in bulk, based on your preferences. You can select from the latest firmware versions and, if necessary, reboot specific devices directly through SageVue. This platform puts you in the driver’s seat regarding system management. It seamlessly integrates with LDAP systems, simplifying the user administration process significantly. You can correlate LDAP groups with user roles in SageVue and specify detailed access permissions. Additionally, SageVue allows for direct configuration of Tesira VoIP-enabled devices without the need for additional software. If you're set to embrace SageVue, rest assured that our support team is ready to assist you every step of the way, ensuring a smooth transition.
API Access
Has API
No
API Access
Has API
No
Integrations
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
Innomate
No
LDAP
No
New Era
No
Xyte
No
Integrations
Amazon SageMaker
No
Amazon Web Services (AWS)
No
Innomate
Yes
LDAP
Yes
New Era
Yes
Xyte
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/pipelines/
Vendor Details
Company Name
Biamp
Founded
1976
Country
United States
Website
www.biamp.com/campaign-pages/sagevue
Product Features
Continuous Delivery
Application Lifecycle Management
No
Application Release Automation
No
Build Automation
No
Build Log
No
Change Management
No
Configuration Management
No
Continuous Deployment
No
Continuous Integration
No
Feature Toggles / Feature Flags
No
Quality Management
No
Testing Management
No
Continuous Integration
Build Log
No
Change Management
No
Configuration Management
No
Continuous Delivery
No
Continuous Deployment
No
Debugging
No
Permission Management
No
Quality Assurance Management
No
Testing Management
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No