We performed a comparison between Amazon SageMaker and Hugging Face based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."They are doing a good job of evolving."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"We were able to use the product to automate processes."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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 tool makes our ML model development a bit more efficient because everything is in one environment."
"The deployment is very good, where you only need to press a few buttons."
"Allows you to create API endpoints."
"My preferred aspects are natural language processing and question-answering."
"What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform."
"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 pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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."
"Lacking in some machine learning pipelines."
"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 solution needs to be cheaper since it now charges per document for extraction."
"The area that needs improvement would be the organization of the materials. It could be clearer and more systematic. It would be good if the layout was clear and we could search the models easily."
"Implementing a cloud system to showcase historical data would be beneficial."
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while Hugging Face is ranked 7th in AI Development Platforms with 3 reviews. Amazon SageMaker is rated 7.4, while Hugging Face is rated 9.0. 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 Hugging Face writes "A comprehensive natural language processing ecosystem offering a diverse range of pre-trained models and a collaborative platform". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI and Domino Data Science Platform, whereas Hugging Face is most compared with Google Vertex AI, Azure OpenAI, Replicate, Google Cloud AI Platform and Microsoft Azure Machine Learning Studio. See our Amazon SageMaker vs. Hugging Face report.
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We monitor all AI Development 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.
