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What is OpsMx Enterprise for Spinnaker?

OES is designed with high availability and scalability, making it ideal for handling increasing deployment workloads while being flexible enough to integrate smoothly with diverse SDLC toolchains. The platform offers an intuitive interface that enables users to define custom stages for executing multiple deployments simultaneously, which greatly minimizes the time required for deployment tasks. Users can easily perform actions such as rolling back, advancing, or pausing all concurrent deployments with just a click, simplifying the management process. Furthermore, OES facilitates the automation of repetitive tasks within the SDLC by allowing the creation of multiple child pipelines that can be initiated from a parent pipeline. Its modular design and API-driven architecture enable OES to serve as a central Continuous Delivery (CD) tool for many organizations. This adaptability also allows developers from different teams to effortlessly link external services with Spinnaker, leading to improved deployment orchestration and enhanced collaboration. Ultimately, OES emerges as a robust solution for refining deployment procedures across a variety of environments, ensuring that teams can respond swiftly to changes in their workflows and project demands. The platform's capability to streamline processes not only boosts productivity but also fosters a more integrated development environment.

What is Amazon SageMaker Pipelines?

Amazon SageMaker Pipelines enables users to effortlessly create machine learning workflows using an intuitive Python SDK while also providing tools for managing and visualizing these workflows via Amazon SageMaker Studio. This platform enhances efficiency significantly by allowing users to store and reuse workflow components, which facilitates rapid scaling of tasks. Moreover, it includes a variety of built-in templates that help kickstart processes such as building, testing, registering, and deploying models, thus making it easier to adopt CI/CD practices within the machine learning landscape. Many users oversee multiple workflows that often include different versions of the same model, and the SageMaker Pipelines model registry serves as a centralized hub for tracking these versions, ensuring that the correct model can be selected for deployment based on specific business requirements. Additionally, SageMaker Studio enables seamless exploration and discovery of models, while users can leverage the SageMaker Python SDK to efficiently access these models, promoting collaboration and boosting productivity among teams. This holistic approach not only simplifies the workflow but also cultivates a flexible environment that accommodates the diverse needs of machine learning practitioners, making it a vital resource in their toolkit. It empowers users to focus on innovation and problem-solving rather than getting bogged down by the complexities of workflow management.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Amazon SageMaker
Ansible
Apache Maven
Aqua
Argo CD
Docker
GitHub
GitLab
Google Cloud Platform
Gradle
HashiCorp Vault
Jenkins
Microsoft Azure
Okta
ServiceNow
Slack
SonarQube Server
Splunk Cloud Platform
Sumo Logic

Integrations Supported

Amazon Web Services (AWS)
Amazon SageMaker
Ansible
Apache Maven
Aqua
Argo CD
Docker
GitHub
GitLab
Google Cloud Platform
Gradle
HashiCorp Vault
Jenkins
Microsoft Azure
Okta
ServiceNow
Slack
SonarQube Server
Splunk Cloud Platform
Sumo Logic

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

OpsMx

Date Founded

2017

Company Location

United States

Company Website

www.opsmx.com/opsmx-enterprise-spinnaker/scalable-and-extensible/

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/sagemaker/pipelines/

Categories and 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 Integration

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

Categories and 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

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