Microsoft Azure Machine Learning vs AWS Sagemaker: Which is Better for Machine Learning?
Machine learning has become an essential technological field in recent years, with many businesses harnessing it to make smarter informed decisions. Before diving into machine learning, it's important to consider the cloud infrastructure available. Two popular companies, Microsoft Azure and Amazon Web Services (AWS), offer excellent machine learning (ML) platforms - Microsoft Azure Machine Learning and AWS Sagemaker. We will provide a comprehensive factual comparison that will help you decide which is better for ML.
Pricing
Cost is one of the importance factors to consider when selecting a machine learning platform. In this regard, Sagemaker and Azure Machine Learning both offer different pricing models with varying features.
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AWS Sagemaker: Amazon offers pay-as-you-go model, which means that you pay for only what you use. However, the pricing can quickly skyrocket, as there are additional charges for storage, data transfer, and spot instances. The monthly cost of running an ML model on Sagemaker ranges from $50 to $1000 for a single-user depending on the size of the data model. (Reference 1)
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Microsoft Azure Machine Learning: Microsoft has a similar pay-as-you-go model, but there are no additional charges for storage, data transfer, or spot instances. A single-user can expect to pay between $30 to $900 monthly for running an ML model on Azure Machine Learning. (Reference 2)
Verdict: Both platforms offer similar pricing models, but Azure Machine Learning is slightly cheaper, especially for users who want to keep their costs low.
Performance
Before making a selection, you want to ensure that the platform you choose offers optimal performance.
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AWS Sagemaker: Sagemaker supports large scale data processing, thus reducing the processing time. Sagemaker also has built-in optimization for GPU instances that are ideal for data-heavy projects.
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Microsoft Azure Machine Learning: Azure Machine Learning can handle low to average ML workloads with ease. However, it is not ideal for high-performance computing that requires additional resources.
Verdict: In terms of performance, Sagemaker has a slight edge over Azure Machine Learning, especially for data-heavy projects.
Features
When it comes to the features available in each platform, there are some similarities and differences.
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AWS Sagemaker: Sagemaker offers many helpful features, including hyperparameter optimization, scalable training, automatic model tuning, and automatic deployment.
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Microsoft Azure Machine Learning: Azure Machine Learning offers automated machine learning, a drag-and-drop interface, an integrated notebook, and other helpful features.
Verdict: Both platforms offer robust features that can aid in the machine learning process. However, Sagemaker offers more advanced features, making it more suitable for more experienced users.
Ease of Use
Ease of use is another factor to consider that can impact the speed and efficiency of your workflow.
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AWS Sagemaker: Sagemaker offers a relatively straightforward learning curve, and the platform is intuitive and easy to use.
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Microsoft Azure Machine Learning: Azure Machine Learning has a higher learning curve, but it offers a feature-rich and customizable interface.
Verdict: AWS Sagemaker offers an intuitive and easy-to-use interface for beginners looking to get started, while Azure Machine Learning offers a more customizable interface for experienced users.
Support
You want to ensure the cloud platform you choose offers support in case of any platform issues.
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AWS Sagemaker: Amazon offers forum-based support to its users, with varying response times depending on the nature of the issue.
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Microsoft Azure Machine Learning: Microsoft offers comprehensive documentation and support to its users, as well as community forums for its users.
Verdict: Both platforms offer support, but Azure Machine Learning offers comprehensive documentation and community forums for users.
Conclusion
In conclusion, Microsoft Azure Machine Learning and AWS Sagemaker are both excellent machine learning platforms. Sagemaker is ideal for data-heavy projects that require high-performance computing, offering more advanced features, while Azure Machine Learning offers a customizable interface, comprehensive support, and a cheaper pricing model for those looking to keep costs low.
It's essential to note that your choice of machine learning platform should depend on your specific requirements, budget, and user experience. We hope that this factual comparison helps you make an informed decision about which is better suited for your needs.
References
- AWS Sagemaker - pricing and features. (2022). Retrieved 27 August 2022, from https://aws.amazon.com/sagemaker/pricing/
- Microsoft Azure Machine Learning - pricing and features. (2022). Retrieved 27 August 2022, from https://azure.microsoft.com/en-us/pricing/details/machine-learning/