Find out what your peers are saying about Databricks, Knime, 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.
In a world surrounded by data, tools that allow navigation of large data volumes ensure decisions are data-driven.
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.
Unfortunately, with Microsoft, you must accept the product as it is.
The availability of GPU instances can be a challenge, requiring proper planning.
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.
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.
It's not a bad grade, as I know of better products in this field.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
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.
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.
The cost for small to medium instances is not very high.
Power BI isn't very cheap, however, it is economical compared to other solutions available.
I found the setup cost to be expensive
These features facilitate rapid development and deployment of AI applications.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
This allows monitoring and performance grading, as I instantly know when someone has a bad call.
In today's data-driven environment, these tools are of substantial value, particularly for large enterprises with numerous processes that require extensive data analysis.
The entire ETL process is easy and supports many databases, allowing data pipelines from multiple sources to be gathered in one place for visualization.
The solution makes it easy for me to develop reports and publish them.
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.
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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