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Compare Azure Machine Learning and IBM Watson Studio 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (51.3% 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)
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningIBM Watson Studio
Star Rating
(164)4.2 out of 5
Market Segments
Enterprise (51.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about IBM Watson StudioSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in "Ease of Deployment" with a score of 9.8, making it particularly user-friendly for teams looking to implement machine learning solutions quickly. In contrast, IBM Watson Studio, while still strong, has a slightly lower score of 9.5 in this area, indicating that users may face a bit more complexity during deployment.
- Reviewers mention that Azure Machine Learning offers superior "Data Source Access" with a breadth score of 8.9 and ease of data connectivity at 9.3, which allows for seamless integration of various data sources. IBM Watson Studio, while competitive, does not match this level of flexibility, which could limit users needing diverse data inputs.
- G2 users highlight Azure Machine Learning's "Quality of Support" with a score of 8.6, indicating a strong customer service experience. Conversely, IBM Watson Studio's support quality, rated at 8.2, suggests that users may not receive the same level of assistance, which could impact their overall experience.
- Users on G2 report that Azure Machine Learning shines in "Model Training" with a score of 8.7, providing robust tools for developing and refining models. IBM Watson Studio, while still effective, has a lower score of 8.3, which may indicate fewer features or less intuitive processes for model training.
- Reviewers mention that Azure Machine Learning's "Scalability" is rated at 9.0, making it a strong choice for enterprises looking to grow their machine learning capabilities. In comparison, IBM Watson Studio's scalability score of 8.6 suggests it may not handle large-scale operations as effectively, which could be a concern for larger organizations.
- Users say that Azure Machine Learning's "No-Code" capabilities are highly praised with a score of 9.7, making it accessible for users without extensive programming knowledge. IBM Watson Studio, while offering no-code options, does not achieve the same level of user-friendliness, which could deter non-technical users from fully utilizing its features.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableIBM Watson Studio
No pricing availableFree Trial
Azure Machine Learning
No trial information availableIBM Watson Studio
No trial information availableRatings
Meets Requirements
8.5
81
8.3
121
Ease of Use
8.5
80
8.0
122
Ease of Setup
8.3
57
7.6
100
Ease of Admin
8.3
49
7.8
95
Quality of Support
8.6
74
8.2
113
Has the product been a good partner in doing business?
8.6
47
8.0
94
Product Direction (% positive)
9.0
80
8.5
115
Features by Category
Data Source Access
Not enough data
9.0
13
Not enough data
9.3
12
Not enough data
9.2
14
Data Interaction
Not enough data
9.0
14
Not enough data
9.2
12
Not enough data
9.4
12
Not enough data
9.1
13
Not enough data
9.2
12
Not enough data
9.2
13
Not enough data
9.1
13
Not enough data
9.6
12
Data Exporting
Not enough data
9.4
12
Not enough data
9.2
12
Not enough data
9.2
12
Generative AI
Not enough data
Not enough data
Deployment
Not enough data
8.8
8
Not enough data
9.2
8
Not enough data
9.0
8
Not enough data
9.4
8
Not enough data
8.8
8
Deployment
Not enough data
9.0
8
Not enough data
8.8
8
Not enough data
8.8
8
Not enough data
9.4
8
Not enough data
9.2
8
Management
Not enough data
9.3
7
Not enough data
9.6
8
Not enough data
9.0
7
Not enough data
9.0
8
Operations
Not enough data
9.0
8
Not enough data
9.0
8
Not enough data
9.3
7
Management
Not enough data
9.5
7
Not enough data
9.4
8
Not enough data
8.8
7
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.6
22
9.0
12
Model Development
8.6
51
8.5
33
8.9
54
8.8
34
8.3
53
8.5
35
8.7
52
8.3
36
Model Development
8.4
21
9.4
13
Machine/Deep Learning Services
8.1
45
8.5
27
7.9
45
8.5
34
7.8
38
Feature Not Available
8.2
42
8.6
28
Machine/Deep Learning Services
8.7
21
8.9
12
8.5
21
9.0
12
Deployment
8.8
50
8.5
32
8.7
51
8.6
33
8.9
51
8.6
30
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
Setup
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
9.7
6
Data
Not enough data
8.6
7
Not enough data
8.6
7
Analysis
Not enough data
9.7
6
Not enough data
8.1
7
Not enough data
8.1
7
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.1
7
Not enough data
7.9
7
Customization
Not enough data
9.0
7
Not enough data
8.1
7
Not enough data
9.2
6
Generative AI
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
Statistical Tool
Not enough data
8.0
14
Not enough data
8.4
15
Not enough data
8.1
15
Data Analysis
Not enough data
8.7
15
Not enough data
9.0
14
Decision Making
Not enough data
8.6
14
Not enough data
8.6
15
Not enough data
8.3
13
Not enough data
8.7
14
Generative AI
Not enough data
9.3
5
Not enough data
8.3
5
Categories
Categories
Shared Categories
Azure Machine Learning and IBM Watson Studio are categorized as MLOps Platforms and Data Science and Machine Learning Platforms
Unique Categories
Azure Machine Learning is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Watson Studio
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%
Information Technology and Services
15.8%
Computer Software
13.3%
Telecommunications
8.2%
Banking
7.6%
Education Management
5.7%
Other
49.4%
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
IBM Watson Studio has no discussions with answers
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