We performed a comparison between Amazon SageMaker and TIBCO Data Science 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 most valuable feature of Amazon SageMaker for me is the model deployment service."
"Allows you to create API endpoints."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"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."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The most valuable feature is the ease of setting up visualizations."
"The most valuable feature is the performance."
"We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost."
"The idea that you don't have to generate reports each day but they are sent automatically is great."
"The solution needs to be cheaper since it now charges per document for extraction."
"The solution is complex to use."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"SageMaker would be improved with the addition of reporting services."
"The documentation must be made clearer and more user-friendly."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"AI is a new area and AWS needs to have an internship training program available."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues."
"Additional templates would help to get things moving more quickly in terms of getting the reports out."
"I would like the visualization for the map of countries to be more easily configurable."
"The scripting for customization could be improved."
Earn 20 points
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while TIBCO Data Science is ranked 25th in Data Science Platforms. Amazon SageMaker is rated 7.4, while TIBCO Data Science is rated 7.6. 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 TIBCO Data Science writes "A straightforward initial setup and good reporting but needs better documentation". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Dataiku, whereas TIBCO Data Science is most compared with TIBCO Statistica, MathWorks Matlab and Dataiku. See our Amazon SageMaker vs. TIBCO Data Science report.
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