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
Our SaaS solution integrates seamlessly with your current CI/CD pipeline, enabling the creation of preview environments and the execution of comprehensive end-to-end tests. When a developer commits code, we swiftly duplicate your stack in mere seconds by utilizing snapshots from prior builds. In one instance of your stack, you can conduct end-to-end testing, while in another, you might build and push Docker images, and yet in a different instance, you can establish temporary review environments. Once a modification has been approved, it can be rapidly deployed to users through your existing deployment pipeline. After a single setup of your stack on webapp.io, you can instantly generate 10 copies, allowing for parallel execution of all your end-to-end and acceptance tests, thus streamlining the development process and enhancing efficiency. The flexibility of our platform ensures that development teams can optimize their workflows and minimize the time between code changes and production deployment.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
Angular
No
Bitbucket
No
BrowserStack
No
ConfigCat
No
Cypress
No
Django
No
Docker
No
GitHub
No
Integrations
Amazon SageMaker
No
Amazon Web Services (AWS)
No
Angular
Yes
Bitbucket
Yes
BrowserStack
Yes
ConfigCat
Yes
Cypress
Yes
Django
Yes
Docker
Yes
GitHub
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
No
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
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/sagemaker/pipelines/
Vendor Details
Company Name
webapp.io
Founded
2018
Country
Canada
Website
webapp.io
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
Product Features
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