Anonymous UserVice President & CIO at a logistics company
Hemant AddalSenior Vice President at a financial services firm
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."
"This open-source product can compete with category leaders in ELT software."
"The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes."
"This solution is easy to use and especially good at data preparation and wrapping."
"It's a coding-less opportunity to use AI. This is the major value for me."
"This solution is easy to use and it can be used to create any kind of model."
"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"It is very fast to develop solutions."
"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."
"The ability to handle large amounts of data and performance in processing need to be improved."
"I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."
"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."
"There should be better documentation and the steps should be easier."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"The predefined workflows could use a bit of improvement."
"The documentation is lacking and it could be better."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
"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."
"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."
"The price of KNIME is quite reasonable and the designer tool can be used free of charge."
"It's an open-source solution."
"The price for Knime is okay."
"At this time, I am using the free version of Knime."
"This is an open-source solution that is free to use."
"There is a Community Edition and paid versions available."
Earn 20 points
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.
Amazon SageMaker is ranked 12th in Data Science Platforms with 5 reviews while KNIME is ranked 3rd in Data Science Platforms with 13 reviews. Amazon SageMaker is rated 7.6, while KNIME is rated 8.4. 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 KNIME writes "Has good machine learning and big data connectivity but the scheduler needs improvement ". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, H2O.ai and Anaconda, whereas KNIME is most compared with Alteryx, RapidMiner, Weka, Dataiku Data Science Studio and Microsoft BI. See our Amazon SageMaker vs. KNIME report.
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