Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Scikit-learn offers a user-friendly and effective suite of tools for predictive data analysis, making it an indispensable resource for those in the field. This powerful, open-source machine learning library is built for the Python programming language and aims to simplify the process of data analysis and modeling. Drawing from established scientific libraries like NumPy, SciPy, and Matplotlib, Scikit-learn presents a diverse array of both supervised and unsupervised learning algorithms, positioning itself as a crucial asset for data scientists, machine learning developers, and researchers alike. Its structure is designed to be both consistent and adaptable, allowing users to mix and match different components to meet their unique requirements. This modularity empowers users to create intricate workflows, streamline repetitive processes, and effectively incorporate Scikit-learn into expansive machine learning projects. Furthermore, the library prioritizes interoperability, ensuring seamless compatibility with other Python libraries, which greatly enhances data processing capabilities and overall efficiency. As a result, Scikit-learn stands out as a go-to toolkit for anyone looking to delve into the world of machine learning.
API Access
Has API
No
API Access
Has API
Yes
Integrations
DagsHub
No
Databricks
No
Flower
No
GLM-5.1
No
GLM-5.2
No
GLM-5.3
No
Guild AI
No
HPE Ezmeral
Yes
Keepsake
No
MLJAR Studio
No
Integrations
DagsHub
Yes
Databricks
Yes
Flower
Yes
GLM-5.1
Yes
GLM-5.2
Yes
GLM-5.3
Yes
Guild AI
Yes
HPE Ezmeral
No
Keepsake
Yes
MLJAR Studio
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
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
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
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
scikit-learn
Country
United States
Website
scikit-learn.org/stable/
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