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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

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

The solution addresses the three key elements of Time, Cost, and Productivity, alleviating concerns for your technology teams. Introducing a new service environment is a straightforward process, as BuildPiper allows effortless modification and duplication of build and deployment specifications from existing environments. This cloning capability significantly streamlines the creation of new environments, making it both rapid and efficient. Additionally, BuildPiper features a well-structured ‘Build Details setup template’ that can easily construct the docker image of the service with just a few straightforward inputs and configurations. For any custom requirements in the docker build process, BuildPiper accommodates them seamlessly! With the inclusion of Pre hooks and Post hooks, it facilitates the execution of personalized steps before and after the Docker image is created. Furthermore, the build template permits the establishment of CI checks right at the build definition stage, ensuring thorough oversight throughout the process. This comprehensive approach not only enhances the efficiency of the build process but also empowers development teams to innovate without being bogged down by the complexities of environment management.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
Docker
Kubernetes

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
Docker
Kubernetes

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/pipelines/

Vendor Details

Company Name

Opstree Solutions

Founded

2014

Country

India

Website

www.opstree.com/buildpiper/

Product Features

Continuous Delivery

Application Lifecycle Management
Application Release Automation
Build Automation
Build Log
Change Management
Configuration Management
Continuous Deployment
Continuous Integration
Feature Toggles / Feature Flags
Quality Management
Testing Management

Continuous Integration

Build Log
Change Management
Configuration Management
Continuous Delivery
Continuous Deployment
Debugging
Permission Management
Quality Assurance Management
Testing Management

Machine Learning

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

Product Features

Application Development

Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development

Continuous Delivery

Application Lifecycle Management
Application Release Automation
Build Automation
Build Log
Change Management
Configuration Management
Continuous Deployment
Continuous Integration
Feature Toggles / Feature Flags
Quality Management
Testing Management

Continuous Integration

Build Log
Change Management
Configuration Management
Continuous Delivery
Continuous Deployment
Debugging
Permission Management
Quality Assurance Management
Testing Management

DevOps

Approval Workflow
Dashboard
KPIs
Policy Management
Portfolio Management
Prioritization
Release Management
Timeline Management
Troubleshooting Reports

Alternatives