We performed a comparison between Amazon SageMaker and IBM SPSS Modeler 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."The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"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."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"They are doing a good job of evolving."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
"It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers."
"The supervised models are valuable. It is also very organized and easy to use."
"Automation is great and this product is very organized."
"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."
"It helped me in that I didn't need to write them by hand, and I could get a result in one or two minutes. That helped me a lot."
"It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly."
"I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
"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."
"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."
"The solution needs to be cheaper since it now charges per document for extraction."
"AI is a new area and AWS needs to have an internship training program available."
"The solution requires a lot of data to train the model."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"The product must provide better documentation."
"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."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"We have run into a few problems doing some entity matching/analytics."
"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"Initial setup of the software was complex, because of our own problems within the government."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. Amazon SageMaker is rated 7.2, while IBM SPSS Modeler is rated 8.0. 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 IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and SAS Enterprise Miner. See our Amazon SageMaker vs. IBM SPSS Modeler report.
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