Microsoft Azure Machine Learning Studio and TensorFlow compete in the machine learning platform category. Based on the comparisons, Microsoft Azure Machine Learning Studio appears to have an advantage in ease of use and deployment due to its intuitive interface and user-friendly features.
Features: Microsoft Azure Machine Learning Studio provides an intuitive drag-and-drop interface, simplifying the creation of machine learning models for non-programmers. It integrates seamlessly with R and Python and offers robust data visualization and deployment capabilities. TensorFlow is valued for its open-source community support, efficient use of GPUs for deep learning, and comprehensive support for numerous machine learning algorithms.
Room for Improvement: Azure Machine Learning Studio could benefit from enhanced prediction features, additional machine learning algorithms, and better data transformation tools. Improvements are also suggested in simplifying its pricing model. TensorFlow users indicate a need for easier model tuning, enhanced CPU optimization, and more seamless version upgrades. Integrating smoothly with programming environments like JavaScript is also a noted area for enhancement.
Ease of Deployment and Customer Service: Azure Machine Learning Studio users find deployment easy on public and hybrid clouds with satisfactory technical support, but some cite a need for improved first-line support. TensorFlow is praised for its comprehensive documentation and versatile deployment across various cloud environments, though it requires more technical expertise than Azure.
Pricing and ROI: Azure Machine Learning Studio offers various pricing options but is considered costly for extensive use, requiring careful plan selection to optimize costs. Users report improved ROI through operational efficiencies. TensorFlow, being open-source, is entirely free and eliminates cost concerns, although support might incur costs if needed. Its value is in providing robust machine learning capabilities at no charge.
| Product | Market Share (%) |
|---|---|
| Microsoft Azure Machine Learning Studio | 4.8% |
| TensorFlow | 5.9% |
| Other | 89.3% |
| Company Size | Count |
|---|---|
| Small Business | 23 |
| Midsize Enterprise | 6 |
| Large Enterprise | 30 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 3 |
| Large Enterprise | 3 |
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.
Microsoft Azure Machine Learning Will Help You:
With Microsoft Azure Machine Learning You Can:
Microsoft Azure Machine Learning Features:
Microsoft Azure Machine Learning Benefits:
Reviews from Real Users:
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.” - Channing S.l, Owner at Channing Stowell Associates
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company
"The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company
"The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
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