We performed a comparison between Amazon SageMaker and Dataiku based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"Allows you to create API endpoints."
"We were able to use the product to automate processes."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The most valuable feature is the set of visual data preparation tools."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"Data Science Studio's data science model is very useful."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The solution is quite stable."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"The product must provide better documentation."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"There are other better solutions for large data, such as Databricks."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"AI is a new area and AWS needs to have an internship training program available."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"The ability to have charts right from the explorer would be an improvement."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"I think it would help if Data Science Studio added some more features and improved the data model."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Dataiku is ranked 7th in Data Science Platforms with 7 reviews. Amazon SageMaker is rated 7.4, while Dataiku is rated 8.2. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of Dataiku writes "Gives different aspects of modeling approaches and good for multiple teams' collaboration". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and DataRobot, whereas Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and SAS Visual Analytics. See our Amazon SageMaker vs. Dataiku report.
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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.
