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Compare Azure Machine Learning and DVC 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Mid-Market (54.5% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Azure Machine Learning
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (38.8% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningDVC
Star Rating
(11)4.7 out of 5
Market Segments
Mid-Market (54.5% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about DVCSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in Ease of Use with a score of 8.6, making it more user-friendly for beginners compared to DVC, which has a lower score of 6.9. Reviewers mention that Azure's intuitive interface simplifies the machine learning workflow.
- Reviewers mention that DVC shines in Versioning with a score of 9.0, allowing for better tracking of changes in data and models. In contrast, Azure Machine Learning's versioning capabilities are rated lower, which can be a drawback for teams focused on reproducibility.
- G2 users highlight Azure Machine Learning's strong Quality of Support with a score of 8.6, indicating that users feel well-supported during their projects. DVC, however, has a lower support rating of 7.3, which some users find lacking when they encounter issues.
- Users on G2 report that Azure Machine Learning offers superior Scalability with a score of 9.2, making it suitable for enterprise-level applications. DVC's scalability score of 7.5 suggests it may not handle large-scale deployments as effectively.
- Reviewers mention that Azure Machine Learning provides a robust Managed Service experience with a score of 8.8, which simplifies deployment and management. DVC, while flexible, does not offer the same level of managed services, which can lead to increased overhead for users.
- Users say that Azure Machine Learning's Deployment Flexibility is a significant advantage, scoring 9.0 for both language and framework flexibility. DVC, while offering some flexibility, does not match Azure's comprehensive support for various programming languages and frameworks, which can limit user options.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableDVC
No pricing availableFree Trial
Azure Machine Learning
No trial information availableDVC
No trial information availableRatings
Meets Requirements
8.5
81
8.9
9
Ease of Use
8.5
80
6.9
9
Ease of Setup
8.3
57
Not enough data
Ease of Admin
8.3
49
Not enough data
Quality of Support
8.6
74
7.3
8
Has the product been a good partner in doing business?
8.6
47
Not enough data
Product Direction (% positive)
9.0
80
10.0
8
Features by Category
Deployment
Not enough data
9.0
5
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
7.6
7
Not enough data
7.5
6
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
7.7
5
Operations
Not enough data
8.1
7
Not enough data
6.4
6
Not enough data
6.9
6
Management
Not enough data
6.9
6
Not enough data
7.1
7
Not enough data
8.3
6
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.6
22
Not enough data
Model Development
8.6
51
Not enough data
8.9
54
Not enough data
8.3
53
Not enough data
8.7
52
Not enough data
Model Development
8.4
21
Not enough data
Machine/Deep Learning Services
8.1
45
Not enough data
7.9
45
Not enough data
7.8
38
Not enough data
8.2
42
Not enough data
Machine/Deep Learning Services
8.7
21
Not enough data
8.5
21
Not enough data
Deployment
8.8
50
Not enough data
8.7
51
Not enough data
8.9
51
Not enough data
Generative AI
8.5
10
Not enough data
8.2
10
Not enough data
7.5
10
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Azure Machine Learning and DVC are categorized as MLOps Platforms
Unique Categories
Azure Machine Learning is categorized as Large Language Model Operationalization (LLMOps), Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
DVC has no unique categories
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
DVC
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Information Technology and Services
28.2%
Computer Software
14.1%
Management Consulting
8.2%
Education Management
5.9%
Higher Education
4.7%
Other
38.8%
Research
18.2%
Computer Software
18.2%
Consulting
9.1%
Oil & Energy
9.1%
Logistics and Supply Chain
9.1%
Other
36.4%
Alternatives
Discussions
What is Azure Machine Learning Studio used for?
1 Comment
AR
In short, to build, deploy, and manage high-quality models faster and with confidence.Read more
Azure Machine Learning has no more discussions with answers
DVC has no discussions with answers
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