We performed a comparison between Amazon SageMaker and Google Cloud AI Platform 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."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 most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"Allows you to create API endpoints."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"The initial setup is very straightforward."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"SageMaker would be improved with the addition of reporting services."
"The solution needs to be cheaper since it now charges per document for extraction."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"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 documentation must be made clearer and more user-friendly."
"There are other better solutions for large data, such as Databricks."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"Customizations are very difficult, and they take time."
"The solution can be improved by simplifying the process to make your own models."
"The initial setup was straightforward for me but could be difficult for others."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while Google Cloud AI Platform is ranked 6th in AI Development Platforms with 7 reviews. Amazon SageMaker is rated 7.4, while Google Cloud AI Platform is rated 7.8. 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 Google Cloud AI Platform writes "An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Cloudera Data Science Workbench, whereas Google Cloud AI Platform is most compared with Azure OpenAI, Microsoft Azure Machine Learning Studio, IBM Watson Machine Learning, Google Vertex AI and OpenVINO. See our Amazon SageMaker vs. Google Cloud AI Platform 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.
