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.
The enterprise subscription offers more benefits, ensuring valuable outcomes.
The technical support from AWS is excellent.
The response time is generally swift, usually within seven to eight hours.
The support is very good with well-trained engineers.
They provide callbacks to ensure clarity and resolution of any queries.
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.
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.
SAS Visual Analytics is stable and manages data effectively without crashing.
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.
Both SageMaker and Lambda are powerful tools, and combining their capabilities could be beneficial.
In terms of configuration, I would like to see AI capabilities since many applications are now integrating AI.
The pricing is high, around an eight.
The cost for small to medium instances is not very high.
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.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
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.
These features facilitate rapid development and deployment of AI applications.
In terms of reporting, it is easy to extract quick reports and provides graphical representations of the data, which is a great feature.
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.
SAS Visual Analytics is a data visualization tool that is used for reporting, data exploration, and analytics. The solution enables users - even those without advanced analytical skills - to understand and examine patterns, trends, and relationships in data. SAS Visual Analytics makes it easy to create and share reports and dashboards that monitor business performance. By using the solution, users can handle, understand, and analyze their data in both past and present fields, as well as influence vital factors for future changes. SAS Visual Analytics is most suitable for larger companies with complex needs.
SAS Visual Analytics Features
SAS Visual Analytics has many valuable key features. Some of the most useful ones include:
SAS Visual Analytics Benefits
There are many benefits to implementing SAS Visual Analytics. Some of the biggest advantages the solution offers include:
Reviews from Real Users
Below are some reviews and helpful feedback written by PeerSpot users currently using the SAS Visual Analytics solution.
A Senior Manager at a consultancy says, “The solution is very stable. The scalability is good. The usability is quite good. It's quite easy to learn and to progress with SAS from an end-user perspective.
PeerSpot user Robert H., Co-owner at Hecht und Heck GmbH, comments, “What I really love about the software is that I have never struggled in implementing it for complex business requirements. It is good for highly sophisticated and specialized statistics in the areas that some people tend to call artificial intelligence. It is used for everything that involves visual presentation and analysis of highly sophisticated statistics for forecasting and other purposes.
Andrea D., Chief Technical Officer at Value Partners, explains, “The best feature is that SAS is not a single BI tool. Rather, it is part of an ecosystem of tools, such as tools that help a user to develop artificial intelligence, algorithms, and so on. SAS is an ecosystem. It's an ecosystem of products. We've found the product to be stable and reliable. The scalability is good.”
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
