Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
DSVMs, or Data Science Virtual Machines, are pre-configured Azure Virtual Machine images equipped with a variety of widely-used tools for data analysis, machine learning, and AI training. They ensure a uniform setup across teams, encouraging seamless collaboration and sharing of resources while leveraging Azure's scalability and management features. Offering a near-zero setup experience, these VMs provide a fully cloud-based desktop environment tailored for data science applications. They facilitate rapid and low-friction deployment suitable for both classroom settings and online learning environments. Users can execute analytics tasks on diverse Azure hardware configurations, benefiting from both vertical and horizontal scaling options. Moreover, the pricing structure allows individuals to pay only for the resources they utilize, ensuring cost-effectiveness. With readily available GPU clusters that come pre-configured for deep learning tasks, users can hit the ground running. Additionally, the VMs include various examples, templates, and sample notebooks crafted or validated by Microsoft, which aids in the smooth onboarding process for numerous tools and capabilities, including but not limited to Neural Networks through frameworks like PyTorch and TensorFlow, as well as data manipulation using R, Python, Julia, and SQL Server. This comprehensive package not only accelerates the learning curve for newcomers but also enhances productivity for seasoned data scientists.
Description
Azure Machine Learning Studio enables organizations to streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with an extensive array of efficient tools for swiftly building, training, and deploying machine learning models. Enhance the speed of market readiness and promote collaboration among teams through leading-edge MLOps—akin to DevOps but tailored for machine learning. Drive innovation within a secure, reliable platform that prioritizes responsible AI practices. Cater to users of all expertise levels with options for both code-centric and drag-and-drop interfaces, along with automated machine learning features. Implement comprehensive MLOps functionalities that seamlessly align with existing DevOps workflows, facilitating the management of the entire machine learning lifecycle. Emphasize responsible AI by providing insights into model interpretability and fairness, securing data through differential privacy and confidential computing, and maintaining control over the machine learning lifecycle with audit trails and datasheets. Additionally, ensure exceptional compatibility with top open-source frameworks and programming languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, thus broadening accessibility and usability for diverse projects. By fostering an environment that promotes collaboration and innovation, teams can achieve remarkable advancements in their machine learning endeavors.
API Access
Has API
No
API Access
Has API
No
Integrations
Azure Marketplace
Yes
MLflow
Yes
Microsoft Azure
Yes
APERIO DataWise
No
Anaconda
Yes
Azure Data Science Virtual Machines
No
Azure Database for MariaDB
No
Azure Kinect DK
No
Azure Percept
No
Cranium
No
Integrations
Azure Marketplace
Yes
MLflow
Yes
Microsoft Azure
Yes
APERIO DataWise
Yes
Anaconda
No
Azure Data Science Virtual Machines
Yes
Azure Database for MariaDB
Yes
Azure Kinect DK
Yes
Azure Percept
Yes
Cranium
Yes
Pricing Details
$0.005
Free Trial
Yes
Free Version
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
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
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
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)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/products/machine-learning/
Product Features
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
No
Product Features
Data Labeling
Human-in-the-loop
Yes
Labeling Automation
Yes
Labeling Quality
Yes
Performance Tracking
Yes
Polygon, Rectangle, Line, Point
Yes
SDK
Yes
Supports Audio Files
Yes
Task Management
Yes
Team Collaboration
Yes
Training Data Management
Yes
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
Yes
Statistical / Mathematical Tools
No
Templates
No
Visualization
No