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Compare Azure Machine Learning and BigML 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (87.5% of reviews)
Entry-Level Pricing
No pricing available
$30 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 LearningBigML
Star Rating
(24)4.7 out of 5
Market Segments
Small-Business (87.5% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
$30 per month
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that BigML excels in ease of use with a score of 9.0, making it particularly appealing for small businesses. Reviewers mention that the drag-and-drop interface simplifies the model development process, allowing users to quickly create and deploy machine learning models without extensive coding knowledge.
- Reviewers mention that Azure Machine Learning shines in scalability, achieving a score of 9.2. Users on G2 highlight its robust infrastructure management capabilities, which are essential for enterprise-level applications that require handling large datasets and complex models efficiently.
- G2 users report that BigML's quality of support is outstanding, with a score of 9.5. Users appreciate the responsive customer service and comprehensive documentation, which help them navigate challenges quickly, enhancing their overall experience with the platform.
- Users say that Azure Machine Learning offers superior integration and extensibility features, particularly with its AI API support and flexibility. Reviewers mention that this allows for seamless integration with existing workflows and third-party applications, making it a strong choice for enterprises looking to leverage their existing tech stack.
- Reviewers mention that BigML's model training capabilities are user-friendly, scoring 8.7, which is beneficial for users who may not have extensive data science backgrounds. Users appreciate the pre-built algorithms that facilitate quick experimentation and deployment of models.
- Users on G2 report that Azure Machine Learning provides advanced features for model monitoring and drift detection, which are crucial for maintaining model performance over time. Reviewers highlight the real-time performance metrics as a key feature that helps teams stay informed about their models' effectiveness.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableBigML
STANDARD
$30
per month
- UNLIMITED TASKS
- MAX. TASK SIZE: 64 MB
- MAX. PARALLEL TASKS: 2
Free Trial
Azure Machine Learning
No trial information availableBigML
No trial information availableRatings
Meets Requirements
8.5
81
9.2
24
Ease of Use
8.5
80
9.0
24
Ease of Setup
8.3
57
9.2
22
Ease of Admin
8.3
49
9.3
22
Quality of Support
8.6
74
9.5
22
Has the product been a good partner in doing business?
8.6
47
9.1
22
Product Direction (% positive)
9.0
80
9.5
24
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
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
Statistical Tool
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Analysis
Not enough data
Not enough data
Not enough data
Not enough data
Decision Making
Not enough data
Not enough data
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
Categories
Categories
Shared Categories
Azure Machine Learning and BigML are categorized as Data Science and Machine Learning Platforms and Low-Code Machine Learning Platforms
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), and Generative AI Infrastructure
BigML is categorized as Predictive Analytics
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
BigML
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
83.3%
Management Consulting
4.2%
International Trade and Development
4.2%
Electrical/Electronic Manufacturing
4.2%
Alternative Dispute Resolution
4.2%
Other
0.0%
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
BigML has no discussions with answers
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