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Average Ratings 0 Ratings

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ease
features
design
support

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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.

Description

A comprehensive platform designed for reproducible and scalable applications in Machine Learning and Deep Learning. Explore the array of features and products that support the leading platform for managing data science workflows today. Polyaxon offers an engaging workspace equipped with notebooks, tensorboards, visualizations, and dashboards. It facilitates team collaboration, allowing members to share, compare, and analyze experiments and their outcomes effortlessly. With built-in version control, you can achieve reproducible results for both code and experiments. Polyaxon can be deployed in various environments, whether in the cloud, on-premises, or in hybrid setups, ranging from a single laptop to container management systems or Kubernetes. Additionally, you can easily adjust resources by spinning up or down, increasing the number of nodes, adding GPUs, and expanding storage capabilities as needed. This flexibility ensures that your data science projects can scale effectively to meet growing demands.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) No 
Caffe No 
Docker No 
Google Cloud Platform No 
HPE Ezmeral Yes 
Keras No 
Kubernetes No 
Microsoft Azure No 
TensorFlow No 

Integrations

Amazon Web Services (AWS) Yes 
Caffe Yes 
Docker Yes 
Google Cloud Platform Yes 
HPE Ezmeral No 
Keras Yes 
Kubernetes Yes 
Microsoft Azure Yes 
TensorFlow 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 Yes 
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 Yes 
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

Hewlett Packard Enterprise

Founded

2015

Country

United States

Website

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

Vendor Details

Company Name

Polyaxon

Website

polyaxon.com

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 

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 

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