Microsoft Azure Machine Learning
What is Microsoft Azure Machine Learning?
Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. Go from idea to deployment in a matter of clicks. Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.
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Real user data aggregated to summarize the product performance and customer experience.
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Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
89 Likeliness to Recommend
100 Plan to Renew
85 Satisfaction of Cost Relative to Value
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Since last award
Emotional Footprint Overview
Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
+92 Net Emotional Footprint
The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.
How much do users love Microsoft Azure Machine Learning?
Pros
- Performance Enhancing
- Respectful
- Includes Product Enhancements
- Reliable
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
Positive
Feature Ratings
Model Training
Data Exploration and Visualization
Pre-Packaged AI/ML Services
Data Pre-Processing
Feature Engineering
Model Tuning
Performance and Scalability
Data Labeling
Model Monitoring and Management
Data Ingestion
Explainability
Vendor Capability Ratings
Breadth of Features
Ease of Data Integration
Quality of Features
Ease of Implementation
Ease of Customization
Availability and Quality of Training
Business Value Created
Ease of IT Administration
Product Strategy and Rate of Improvement
Usability and Intuitiveness
Vendor Support
Microsoft Azure Machine Learning Reviews
Hosam K.
- Role: Human Resources
- Industry: Consulting
- Involvement: Business Leader or Manager
Submitted Jul 2023
Easy to use Machine Learning Platform.
Likeliness to Recommend
What differentiates Microsoft Azure Machine Learning from other similar products?
Azure Machine Learning integrates seamlessly with other Azure services, such as Azure Data Science Virtual Machine and Azure Cognitive Services, which facilitates machine learning workflows and model training within the Azure ecosystem.
What is your favorite aspect of this product?
Azure Machine Learning is cloud-based, easy to use, and has advanced capabilities in building and deploying machine learning models. It can import data from Azure storage and has a flexible pricing model. It offers Machine Learning as a Service that supports a comprehensive range of algorithms and integrates with cross-platform tools and frameworks.
What do you dislike most about this product?
Azure Machine Learning has limited storage in the free version. The platform can be complex and confusing, especially for users new to Azure Cloud and machine learning.
What recommendations would you give to someone considering this product?
It would be great to start by understanding the basics of machine learning, including its algorithms and techniques, and Familiarize yourself with the Azure Machine Learning service and its features. Take advantage of the official learning resources, tutorials, and documentation.
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Performance Enhancing
Sashank M.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Jun 2023
Ease of Use and UI friendly
Likeliness to Recommend
What differentiates Microsoft Azure Machine Learning from other similar products?
Integration with Azure Ecosystem: AML seamlessly integrates with the broader Azure ecosystem, providing access to a wide range of Azure services, including data storage, compute resources, data pipelines, and analytics tools. This integration allows users to build end-to-end machine learning solutions using a unified platform. Scalability and Flexibility: AML provides scalable and flexible infrastructure to support a variety of workloads. Users can choose from a range of virtual machine sizes, including GPU instances for deep learning tasks.
What is your favorite aspect of this product?
Ease of use: Azure Machine Learning offers a user-friendly interface and a wide range of tools that simplify the end-to-end machine learning workflow. It provides a drag-and-drop designer for creating machine learning pipelines without writing extensive code. Additionally, it integrates with popular data science libraries such as TensorFlow, PyTorch, and scikit-learn, allowing you to leverage your existing knowledge and code. Broad toolset and framework support: Azure Machine Learning supports various programming languages (Python, R, etc.) and provides integration with popular development tools like Jupyter Notebooks and Visual Studio Code.
What do you dislike most about this product?
Learning Curve: While Azure Machine Learning provides a user-friendly interface and tools, it can still have a learning curve, especially for users who are new to the Azure ecosystem or machine learning concepts. Understanding the platform's features, navigating the documentation, and effectively utilizing all the capabilities may require some initial investment in learning. Cost: Azure Machine Learning is a cloud-based service, and the cost associated with using it can vary depending on the resources consumed. Users should carefully consider the pricing model, compute resources, storage, and other factors to estimate and manage costs.
What recommendations would you give to someone considering this product?
Understand your requirements: Clearly define your project requirements, goals, and constraints. Consider the size and complexity of your datasets, the scale of your machine learning workflows, and the specific tools and frameworks you plan to use. This will help you assess whether Azure Machine Learning is a good fit for your needs. Familiarize yourself with Azure ecosystem: Get acquainted with the broader Azure ecosystem and services. Understand how Azure Machine Learning integrates with other Azure services such as Azure Data Lake Storage, Azure Databricks, and Azure Cognitive Services. This will enable you to leverage the full potential.
Pros
- Continually Improving Product
- Reliable
- Performance Enhancing
- Enables Productivity
Kevin S.
- Role: Information Technology
- Industry: Technology
- Involvement: End User of Application
Submitted Jun 2023
Review for Microsoft Azure Machine Learning
Likeliness to Recommend
What differentiates Microsoft Azure Machine Learning from other similar products?
Its ease of use is very good with deployment and monitoring of the ML models is very reliable
What is your favorite aspect of this product?
Deployment and Monitoring of data
What do you dislike most about this product?
Ita offline version is not yet released so sometimes lags when internet connectivity is not upto the mark.
What recommendations would you give to someone considering this product?
Highly recommendable for the in-built pre-packaged AI/ML models available in the cloud services
Pros
- Reliable
- Performance Enhancing
- Unique Features
- Inspires Innovation
