Introducing G2.ai, the future of software buying.Try now
Compare Azure Machine Learning and IBM Cloud Pak for Data 
Featured Products
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
Alteryx
Visit Website
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
ILUM
Visit Website
Sponsored
You’re seeing this ad based on the product’s relevance to this page. Sponsored content does not receive preferential treatment in any of G2’s ratings.
Starburst
Visit Website
At a Glance
Market Segments
Enterprise (38.8% of reviews)
Enterprise (50.0% 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 LearningIBM Cloud Pak for Data
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (50.0% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Cloud Pak for DataSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in its Ease of Use with a score of 8.6, making it more user-friendly for beginners compared to IBM Cloud Pak for Data, which has a score of 8.1. Reviewers mention that the intuitive interface and comprehensive documentation significantly enhance the user experience.
- Reviewers mention that IBM Cloud Pak for Data shines in Data Ingestion & Wrangling, scoring 9.5, which is notably higher than Azure Machine Learning's score of 8.7. Users appreciate the robust tools for data preparation and integration, allowing for seamless data workflows.
- G2 users highlight Azure Machine Learning's superior Model Development capabilities, particularly in Pre-Built Algorithms, where it scores 8.3 compared to IBM's 9.1. Users say that the variety and effectiveness of pre-built models in IBM Cloud Pak for Data provide a significant advantage for rapid deployment.
- Users on G2 report that Azure Machine Learning offers better Scalability with a score of 9.2, while IBM Cloud Pak for Data scores 9.1. Reviewers mention that Azure's ability to handle large datasets and scale resources dynamically is a key feature for enterprise-level applications.
- Reviewers mention that IBM Cloud Pak for Data provides a more comprehensive Managed Service experience, scoring 9.3 compared to Azure's 8.8. Users appreciate the ease of management and support provided by IBM, which simplifies the deployment and maintenance of machine learning models.
- Users report that Azure Machine Learning's Customization options, particularly in Custom VMs, score 8.0, allowing for tailored environments. In contrast, IBM Cloud Pak for Data's flexibility in Application Deployment is noted as a strong point, with both products scoring equally at 8.8, indicating a balanced offering in this area.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableIBM Cloud Pak for Data
No pricing availableFree Trial
Azure Machine Learning
No trial information availableIBM Cloud Pak for Data
No trial information availableRatings
Meets Requirements
8.5
81
8.5
47
Ease of Use
8.5
80
8.1
47
Ease of Setup
8.3
57
7.2
26
Ease of Admin
8.3
49
7.6
27
Quality of Support
8.6
74
8.3
42
Has the product been a good partner in doing business?
8.6
47
8.1
25
Product Direction (% positive)
9.0
80
8.8
47
Features by Category
Customization
Not enough data
8.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure
Not enough data
Not enough data
Not enough data
9.0
5
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
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
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
Infrastructure Provision
Not enough data
7.7
8
Not enough data
8.1
7
Not enough data
7.9
7
Not enough data
8.3
6
Not enough data
8.5
8
Not enough data
7.9
8
Not enough data
8.3
8
Not enough data
8.3
7
Management
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
8.1
8
Functionality
Not enough data
8.3
8
System
8.6
22
9.5
11
Model Development
8.6
51
8.3
8
8.9
54
8.5
8
8.3
53
9.1
9
8.7
52
8.8
8
Model Development
8.4
21
8.5
12
Machine/Deep Learning Services
8.1
45
8.7
9
7.9
45
9.4
8
7.8
38
9.6
8
8.2
42
9.0
7
Machine/Deep Learning Services
8.7
21
9.2
8
8.5
21
9.3
7
Deployment
8.8
50
9.3
9
8.7
51
9.3
9
8.9
51
8.9
9
Generative AI
8.5
10
8.3
5
8.2
10
8.3
5
7.5
10
8.3
5
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
Data Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
8.3
5
Analytics
Not enough data
Not enough data
Security
Not enough data
9.0
5
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 - Data Fabric
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
Statistical Tool
Not enough data
8.3
5
Not enough data
8.8
8
Not enough data
8.6
7
Data Analysis
Not enough data
8.7
9
Not enough data
9.0
7
Decision Making
Not enough data
8.3
6
Not enough data
8.8
7
Not enough data
8.8
8
Not enough data
8.3
8
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Data Transformation
Not enough data
8.5
27
|
Verified
Not enough data
9.1
15
|
Verified
Connectivity
Not enough data
8.0
23
|
Verified
Not enough data
8.6
22
|
Verified
Not enough data
8.1
25
|
Verified
Not enough data
8.7
24
|
Verified
Operations
Not enough data
8.7
26
|
Verified
Not enough data
8.9
25
|
Verified
Not enough data
8.4
23
|
Verified
Not enough data
8.8
24
|
Verified
Not enough data
8.7
13
|
Verified
Building Reports
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
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
Categories
Categories
Shared Categories
Azure Machine Learning and IBM Cloud Pak for Data are categorized as Data Science and Machine Learning Platforms and Generative AI Infrastructure
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), and Low-Code Machine Learning Platforms
IBM Cloud Pak for Data is categorized as Data Fabric, Virtual Private Cloud (VPC), Big Data Analytics, Predictive Analytics, and Infrastructure as a Service (IaaS)
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Cloud Pak for Data
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%
Computer Software
12.9%
Information Technology and Services
8.6%
Banking
8.6%
Financial Services
5.7%
Education Management
5.7%
Other
58.6%
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 Cloud Pak for Data has no discussions with answers
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
