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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

GitHub Packages allows you to publish and consume packages securely, whether within your organization or for a global audience. By utilizing standard package managers along with native commands, you can seamlessly authenticate and publish your packages directly to GitHub. It's essential to comprehend and install package contents safely while sourcing them from the community on GitHub, ensuring that only approved packages are utilized within your organization. All your packages can be stored in a secure environment alongside your source code, safeguarded by your GitHub credentials. Additionally, with comprehensive API and webhook support, your workflows can be enhanced to integrate smoothly with GitHub Packages. The platform employs advanced edge caching through a global CDN, ensuring optimal performance regardless of your build locations. Moreover, you can leverage Actions to automate the publication of new package versions to GitHub Packages, facilitating smoother CI/CD processes. This enables you to install packages and images not only from GitHub Packages but also from your preferred registry, thereby enriching your development experience. By consolidating packaging and source control under one roof, GitHub Packages streamlines collaboration and enhances productivity across teams.

Description

HPE Ezmeral ML Ops offers a suite of integrated tools designed to streamline machine learning workflows throughout the entire ML lifecycle, from initial pilot stages to full production, ensuring rapid and agile operations akin to DevOps methodologies. You can effortlessly set up environments using your choice of data science tools, allowing you to delve into diverse enterprise data sources while simultaneously testing various machine learning and deep learning frameworks to identify the most suitable model for your specific business challenges. The platform provides self-service, on-demand environments tailored for both development and production tasks. Additionally, it features high-performance training environments that maintain a clear separation between compute and storage, enabling secure access to shared enterprise data, whether it resides on-premises or in the cloud. Moreover, HPE Ezmeral ML Ops supports source control through seamless integration with popular tools like GitHub. You can manage numerous model versions—complete with metadata—within the model registry, facilitating better organization and retrieval of your machine learning assets. This comprehensive approach not only optimizes workflow management but also enhances collaboration among teams.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

GitHub Yes 
HPE Ezmeral No 
Harbor Yes 

Integrations

GitHub No 
HPE Ezmeral Yes 
Harbor No 

Pricing Details

$0.25 per GB
Free Trial Yes 
Free Version Yes 

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 Yes 
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 No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

GitHub

Country

United States

Website

github.com/features/packages

Vendor Details

Company Name

Hewlett Packard Enterprise

Founded

2015

Country

United States

Website

www.hpe.com/us/en/solutions/ezmeral-machine-learning-operations.html

Product Features

Product Features

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 

Alternatives

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