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 few projects we have done have been promising."
"They are doing a good job of evolving."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"Allows you to create API endpoints."
"The deployment is very good, where you only need to press a few buttons."
"The Cloudera Data Science Workbench is customizable and easy to use."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"AI is a new area and AWS needs to have an internship training program available."
"Lacking in some machine learning pipelines."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"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.
Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.
Amazon SageMaker is ranked 13th in Data Science Platforms with 5 reviews while Cloudera Data Science Workbench is ranked 17th in Data Science Platforms with 1 review. Amazon SageMaker is rated 7.6, while Cloudera Data Science Workbench is rated 8.0. 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, the top reviewer of Cloudera Data Science Workbench writes "Customizable, easy to install, and easy to use". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, H2O.ai and Alteryx, whereas Cloudera Data Science Workbench is most compared with Databricks, Alteryx, Anaconda, Dataiku Data Science Studio and KNIME.
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