Anonymous UserVice President & CIO at a logistics company
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"The support costs are 10% of the Amazon fees and it comes by default."
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.
WPS is used in production on a huge range of systems at sites around the world. Thousands of analysts are using WPS in their daily data processing and analysis tasks, and thousands more rely on the heavy-lifting ability of WPS overnight and scheduled processing to deliver the information they require. Customers are able to choose to run WPS software on IBM z/OS Mainframes, servers and workstations running UNIX, Linux, Solaris, AIX, macOS and the full range of Microsoft Windows operating systems to fit in with existing or planned infrastructure.
Amazon SageMaker is ranked 13th in Data Science Platforms with 5 reviews while WPS Analytics is ranked 26th in Data Science Platforms. Amazon SageMaker is rated 7.6, while WPS Analytics is rated 0.0. The top reviewer of Amazon SageMaker writes "A solution with great computational storage, has many pre-built models, is stable, and has good support". On the other hand, Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, Domino Data Science Platform and H2O.ai, whereas WPS Analytics is most compared with SAS Visual Analytics, Alteryx, SAS Visual Data Mining and Machine Learning, RapidMiner and Databricks.
See our list of best Data Science Platforms vendors.
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