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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (54.8% of reviews)
Entry-Level Pricing
No pricing available
Free 1 User Per Month
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 LearningWeights & Biases
Star Rating
(44)4.7 out of 5
Market Segments
Small-Business (54.8% of reviews)
Pros & Cons
Entry-Level Pricing
Free 1 User Per Month
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in its Managed Service capabilities, scoring 8.8, which allows for seamless integration and management of machine learning workflows. In contrast, Weights & Biases also scores 8.8 but is noted for its Application features, which provide a more user-friendly interface for small businesses.
- Reviewers mention that Azure Machine Learning's Scalability is rated at 8.3, which is adequate for enterprise-level applications, but users on G2 highlight that Weights & Biases shines with a higher score of 9.2, making it more suitable for projects that require rapid scaling.
- G2 users indicate that Azure Machine Learning's Quality of Support is rated at 8.6, which is commendable, but reviewers mention that Weights & Biases offers superior support with a score of 9.1, emphasizing their responsiveness and helpfulness in troubleshooting issues.
- Users say that Azure Machine Learning provides a solid Model Registry feature with a score of 8.5, which is essential for managing model versions. However, Weights & Biases, with a slightly lower score of 8.3, is still praised for its ease of use in cataloging and monitoring models.
- Reviewers mention that Azure Machine Learning's Ease of Setup is rated at 8.4, which some find slightly challenging, while users on G2 report that Weights & Biases offers a more intuitive setup process with a score of 9.1, making it more accessible for newcomers.
- Users report that Azure Machine Learning's Language Support is rated at 8.9, which is beneficial for diverse programming needs, but reviewers mention that Weights & Biases also provides robust support with a score of 8.6, particularly for Python users, enhancing its appeal in the data science community.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableWeights & Biases
Personal
Free
1 User Per Month
Weights & Biases is free for personal projects.
- Unlimited Experiments
- Unlimited Tracked Hours
- 100 GB storage and artifacts tracking
Free Trial
Azure Machine Learning
No trial information availableWeights & Biases
No trial information availableRatings
Meets Requirements
8.5
81
9.0
37
Ease of Use
8.5
80
8.9
37
Ease of Setup
8.3
57
9.0
24
Ease of Admin
8.3
49
Not enough data
Quality of Support
8.6
74
9.2
28
Has the product been a good partner in doing business?
8.6
47
Not enough data
Product Direction (% positive)
9.0
80
8.4
36
Features by Category
Deployment
Not enough data
8.1
28
Not enough data
8.5
27
Not enough data
8.5
26
Not enough data
8.7
27
Not enough data
8.3
27
Deployment
Not enough data
8.3
26
Not enough data
8.6
27
Not enough data
8.5
25
Not enough data
8.3
27
Not enough data
8.2
27
Management
Not enough data
8.6
26
Not enough data
9.3
28
Not enough data
7.7
25
Not enough data
8.4
24
Operations
Not enough data
8.9
28
Not enough data
7.7
25
Not enough data
8.5
28
Management
Not enough data
8.3
26
Not enough data
9.3
28
Not enough data
7.6
23
Generative AI
Not enough data
5.6
9
Not enough data
5.9
9
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 Weights & Biases 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
Weights & Biases 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.)
Weights & Biases
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
31.0%
Computer Software
28.6%
Biotechnology
9.5%
Information Technology and Services
4.8%
Consumer Electronics
4.8%
Other
21.4%
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
Weights & Biases has no discussions with answers
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