Find out what your peers are saying about Databricks, Dataiku, Amazon Web Services (AWS) and others in Data Science Platforms.
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.
Amazon SageMaker definitely provides ROI.
In a world surrounded by data, tools that allow navigation of large data volumes ensure decisions are data-driven.
Because of integration with Microsoft Power BI, its interactive dashboards, and how easily it integrates with other SQL data sources, business decisions from business users have become much faster.
Power BI is easy to deploy within an hour, providing robust security against data leaks.
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 significant drawback I notice is that Microsoft's size makes it hard to get specific change requests addressed unless they involve a bug.
We have a partnership with Microsoft, involving multiple weekly calls with dedicated personnel to ensure our satisfaction.
The support is good because there is also a community available.
The availability of GPU instances can be a challenge, requiring proper planning.
It works very well with large data sets from one terabyte to fifty terabytes.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
You expect only a small percentage of users concurrently, but beyond a thousand concurrent users, it becomes difficult to manage.
With increasing AI capabilities, architectural developments within Microsoft, and tools like Fabric, I expect Power BI to scale accordingly.
As more data is processed, performance issues may arise.
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.
In terms of stability, there's no data loss or leakage, and precautions are well-managed by Microsoft.
Earlier it used to take six months to one year; now we can do it in one week.
We typically do not have problems with end-user tools like Excel and Power BI.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
This makes Power BI difficult to manage as loading times can reach one or two minutes, which is problematic today.
Access was more logical in how it distinguished between data and its formatting.
Microsoft updates Power BI monthly based on user community feedback.
The cost for small to medium instances is not very high.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
I found the setup cost to be expensive
Power BI isn't very cheap, however, it is economical compared to other solutions available.
The pricing for Microsoft Power BI is low, which is a good selling point.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
They offer insights into everyone making calls in my organization.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
In today's data-driven environment, these tools are of substantial value, particularly for large enterprises with numerous processes that require extensive data analysis.
Within the organization, Microsoft Power BI is used to create dashboards and gain insights into data, enhancing data-driven decision-making.
To reduce the need for highly skilled personnel, we can engage someone who is just familiar and has a basic understanding of Microsoft Power BI, while AI can handle the major tasks through either agent AI or requirement analysis.
| Product | Mindshare (%) |
|---|---|
| Amazon SageMaker | 3.3% |
| Databricks | 7.2% |
| Dataiku | 4.8% |
| Other | 84.7% |
| Product | Mindshare (%) |
|---|---|
| Microsoft Power BI | 7.4% |
| Tableau Enterprise | 5.9% |
| Amazon QuickSight | 3.1% |
| Other | 83.6% |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 11 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 137 |
| Midsize Enterprise | 59 |
| Large Enterprise | 171 |
Amazon SageMaker accelerates machine learning workflows by offering features like Jupyter Notebooks, AutoML, and hyperparameter tuning, while integrating seamlessly with AWS services. It supports flexible resource selection, effective API creation, and smooth model deployment and scaling.
Providing a comprehensive suite of tools, Amazon SageMaker simplifies the development and deployment of machine learning models. Its integration with AWS services like Lambda and S3 enhances efficiency, while SageMaker Studio, featuring Model Monitor and Feature Store, supports streamlined workflows. Users call for improvements in IDE maturity, pricing, documentation, and enhanced serverless architecture. By addressing scalability, big data integration, GPU usage, security, and training resources, SageMaker aims to better assist in machine learning demands and performance optimization.
What features does Amazon SageMaker offer?In industries like finance, retail, and healthcare, Amazon SageMaker supports training and deploying machine learning models for outlier detection, image analysis, and demand forecasting. It aids in chatbot implementation, recommendation systems, and predictive modeling, enhancing data science collaboration and leveraging compute resources efficiently. Tools like Jupyter notebooks, Autopilot, and BlazingText facilitate streamlined AI model management and deployment, increasing productivity and accuracy in industry-specific applications.
Microsoft Power BI is a powerful tool for data analysis and visualization. This tool stands out for its ability to merge and analyze data from various sources. Widely adopted across different industries and departments, Power BI is instrumental in creating visually appealing dashboards and generating insightful business intelligence reports. Its intuitive interface, robust visualization capabilities, and seamless integration with other Microsoft applications empower users to easily create interactive reports and gain valuable insights.
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