Microsoft Azure Machine Learning Studio and Amazon SageMaker are prominent in cloud-based machine learning, each excelling in different aspects. User reviews indicate a preference for Amazon SageMaker for its extensive integration capabilities within AWS, particularly for users deeply invested in the AWS ecosystem.
Features: Microsoft Azure Machine Learning Studio provides an intuitive drag-and-drop interface that suits varying technical skills and integrates well with other Microsoft services. It also offers robust cognitive service options through prebuilt models. Amazon SageMaker offers a flexible solution with comprehensive support for various machine learning workflows, benefiting from extensive AWS service integration. It provides a feature-rich environment with tools such as SageMaker Canvas and Feature Store, making it suitable for advanced users and those needing robust AI model management.
Room for Improvement: Microsoft Azure Machine Learning Studio could improve by expanding prediction capabilities and simplifying deployments outside its environment. Enhancing data transformation features would increase flexibility. Amazon SageMaker's primary area for improvement includes reducing costs for heavy workloads and addressing documentation issues to aid beginners in navigating the platform more effectively.
Ease of Deployment and Customer Service: Both platforms leverage public cloud infrastructure with limited private or hybrid cloud options. Microsoft Azure Machine Learning Studio is usually commended for its customer service and technical support, though initial support may sometimes be challenging. Amazon SageMaker offers strong technical support but has documentation gaps that some users find challenging during deployment.
Pricing and ROI: Microsoft Azure Machine Learning Studio is perceived as cost-effective with its pay-per-use model but may incur hidden costs with extensive usage. Amazon SageMaker operates with a pay-as-you-go model, often resulting in higher costs. However, its value within the AWS ecosystem can justify the investment for those already committed to AWS, despite being pricier.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
I have seen a return on investment from using Microsoft Azure Machine Learning Studio in terms of workload reduction, as we now complete the same projects with two people instead of five.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
The response time is generally swift, usually within seven to eight hours.
The customer support for Microsoft Azure Machine Learning Studio is quite responsive across different channels, making it a cool experience.
Microsoft technical support is rated a seven out of ten.
It works very well with large data sets from one terabyte to fifty terabytes.
The availability of GPU instances can be a challenge, requiring proper planning.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
Microsoft Azure Machine Learning Studio is scalable as I can choose the compute, making it flexible for various scales.
We are building Azure Machine Learning Studio as a scalable solution.
Microsoft Azure Machine Learning Studio's scalability has been beneficial, as I could increase my compute resources when needing more data injection.
There are issues, but they are easily detectable and fixable, with smooth error handling.
The product has been stable and scalable.
I rate the stability of Amazon SageMaker between seven and eight.
Microsoft Azure Machine Learning Studio is stable;
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
Both SageMaker and Lambda are powerful tools, and combining their capabilities could be beneficial.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
It would be beneficial for them to incorporate more services required for LLMs or LLM evaluation.
There is always room for improvement, and I expect Microsoft Azure Machine Learning Studio to continue iterating and focusing on a human-centric design approach.
I find the pricing to be not a good story in this case, as it is not affordable for everyone.
The pricing is high, around an eight.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The cost for small to medium instances is not very high.
I rate the pricing as three or four on a scale of one to ten in terms of affordability.
The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
This allows monitoring and performance grading, as I instantly know when someone has a bad call.
SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding.
The platform provides managed services and compute, and I have more control in Azure, even in terms of monitoring services.
Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints.
| Product | Market Share (%) |
|---|---|
| Amazon SageMaker | 4.0% |
| Microsoft Azure Machine Learning Studio | 3.4% |
| Other | 92.6% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 17 |
| Company Size | Count |
|---|---|
| Small Business | 23 |
| Midsize Enterprise | 6 |
| Large Enterprise | 30 |
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
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
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