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Compare Azure Machine Learning and IBM Decision Optimization 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (60.0% of reviews)
Entry-Level Pricing
No pricing available
Free
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 Decision Optimization
Star Rating
(41)4.5 out of 5
Market Segments
Enterprise (60.0% of reviews)
Entry-Level Pricing
Free
Browse all 2 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in scalability with a score of 9.2, allowing for efficient handling of large datasets and complex models, while IBM Decision Optimization scores lower at 8.8, indicating potential limitations in scaling for larger enterprise needs.
- Reviewers mention that Azure Machine Learning offers superior data ingestion and wrangling capabilities with a score of 8.7, making it easier to prepare data for analysis, whereas IBM Decision Optimization's score of 7.2 suggests that users may face challenges in this area.
- G2 users highlight Azure Machine Learning's drag and drop interface with a score of 8.9, which simplifies the model development process, while IBM Decision Optimization's score of 8.3 indicates a less intuitive user experience in this regard.
- Reviewers say that Azure Machine Learning's model training capabilities are rated at 8.8, showcasing its effectiveness in training complex models, compared to IBM Decision Optimization's lower score of 7.8, which may reflect a less robust training environment.
- Users on G2 report that Azure Machine Learning provides a more comprehensive pre-built algorithms library with a score of 8.3, facilitating quicker model development, while IBM Decision Optimization matches this score but lacks the same breadth of options, potentially limiting user flexibility.
- Reviewers mention that Azure Machine Learning's quality of support is rated at 8.6, indicating a strong support system for users, while IBM Decision Optimization also scores 8.6, but users have noted that the responsiveness can vary, suggesting inconsistency in user experiences.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableIBM Decision Optimization
IBM ILOG CPLEX Optimization Studio Free edition
Free
Explore and learn optimization and constraint programming modeling with our free edition. Try free edition:
https://www.ibm.com/account/reg/signup?formid=urx-20028
- Limited to 1000 variables and 1000 constraints, but models can be edited
- Use both the OPL/IDE and API modeling/solving
Free Trial
Azure Machine Learning
No trial information availableIBM Decision Optimization
Free Trial is available
Ratings
Meets Requirements
8.5
81
8.4
30
Ease of Use
8.5
80
8.7
30
Ease of Setup
8.3
57
8.3
14
Ease of Admin
8.3
49
8.7
14
Quality of Support
8.6
74
8.6
30
Has the product been a good partner in doing business?
8.6
47
9.2
14
Product Direction (% positive)
9.0
80
8.0
29
Features by Category
Deployment
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
Deployment
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
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Operations
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
8.6
22
7.2
9
Model Development
8.6
51
7.9
7
8.9
54
8.3
7
8.3
53
8.3
7
8.7
52
7.4
7
Model Development
8.4
21
7.4
9
Machine/Deep Learning Services
8.1
45
7.9
7
7.9
45
7.4
7
7.8
38
6.9
7
8.2
42
6.9
7
Machine/Deep Learning Services
8.7
21
7.3
8
8.5
21
7.5
8
Deployment
8.8
50
8.3
7
8.7
51
8.1
7
8.9
51
7.6
7
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
Integration
Not enough data
Not enough data
Not enough data
Not enough data
Business Logic
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Analytics
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Decision Management 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 IBM Decision Optimization are categorized as Data Science and Machine Learning Platforms
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, and Low-Code Machine Learning Platforms
IBM Decision Optimization is categorized as Decision Management Platforms
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Decision Optimization
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%
Financial Services
14.3%
Education Management
11.4%
Computer Software
11.4%
Transportation/Trucking/Railroad
5.7%
Research
5.7%
Other
51.4%
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
IBM Decision Optimization has no discussions with answers
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