Overview
Product Information on Amazon SageMaker AI
What is Amazon SageMaker AI?
Amazon SageMaker AI Pricing
Overall experience with Amazon SageMaker AI
“SageMaker AI Excels in Speed and Scalability but Interface Lags on Heavy Loads”
“Easy Setup Appreciated, Yet Local Task Isolation Remains a Desired Improvement”
About Company
Company Description
Amazon Web Services (AWS), established in 2006, is focused on providing essential infrastructure services to businesses globally in the form of cloud computing. The key advantage offered through cloud computing, particularly via AWS, is its capacity to shift fixed infrastructure expenses into flexible costs. Businesses have been able to forgo extensive planning and procurement of servers and other Information Technology (IT) resources, owing to AWS. AWS seeks to provide businesses with prompt and cost-effective access to resources using Amazon's expertise and economies of scale, as and when their business requires. Currently, AWS offers a robust, scalable, economic infrastructure platform on the cloud powering an extensive array of businesses worldwide. It operates across numerous industries with data center locations in various parts of the globe including U.S., Europe, Singapore, and Japan.
Company Details
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
Reviewer Insights for: Amazon SageMaker AI
Deciding Factors: Amazon SageMaker AI Vs. Market Average
Amazon SageMaker AI Likes & Dislikes
- The performance is top notch - Easy to scale the resources - Integration with other AWS services - The environment setup is clean and reproducible - great for testing and collaboration
easy to use and get up and running
Easy to deploy models Easy to use already existing Foundation Models For practitioners and experts
The main area that could use improvement is the SageMaker notebook interface. It sometimes feels a bit clunky. Switching kernels or running heavier cells can freeze up the environment. There's also a slight learning curve when configuring permissions or networking between AWS services inside the SageMaker environment.
less product offering and more core product that are high roi
Requires more documentation about how to start using it Documentation is more focused for ML Engineers
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Peer Discussions
Amazon SageMaker AI Reviews and Ratings
- SOFTWARE DEVELOPER50M-1B USDHealthcare and BiotechReview Source
SageMaker AI Excels in Speed and Scalability but Interface Lags on Heavy Loads
My overall experience with using SageMaker AI has been very fulfilling and positive. I primarily use it within the AWS environment for development and testing because of internal company policies that restrict AWS services from outside environments. It's been extremely helpful for experimenting with AI agent architectures and RAG pipelines. The compute speed and scalability are excellent. Training and inference tasks run much faster compared to local setups. The only real challenge I've faced is with the SageMaker notebook UI, which still feels a bit unpolished and sometimes lags when handling heavier workloads or switching kernels. - Software Developer50M-1B USDHealthcare and BiotechReview Source
AWS MLOps Workflow Offers Easy Model Deployment But Lacks Beginner Documentation
Build a complete MLOps workflow in an easy-way with all the power of AWS - Engineer<50M USDServices (non-Government)Review Source
Easy Setup Appreciated, Yet Local Task Isolation Remains a Desired Improvement
it works really well for our use case but it would be nice if there was a way to do things in isolation locally - Engineer50M-1B USDBankingReview Source
Effective CloudWatch Integration and Scaled Model Handling With Cost Monitoring Needs
good integration with cloudwatch, able to handle large scaled models, and run at scale - Founder<50M USDIT ServicesReview Source
Automated Model Training Receives Positive Feedback Despite Concerns Over Price
good for automation and pipelines for training not so good with AIM and roles