What is our primary use case?
The primary use cases for Amazon SageMaker are for EDA processing, ML model building, setting up MLOps, predictive analysis, customer churn models, fraud detection, image and video analysis, as well as NLP projects. It is a versatile tool in the machine-learning landscape.
What is most valuable?
The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities. These features allow for generating high-quality models without needing extensive coding knowledge, making it accessible for non-experts. SageMaker helps in end-to-end machine learning, incorporating data preparation, model deployment, and continuous monitoring.
What needs improvement?
Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker. It is considered complex to integrate these aspects, and adjustments need to be made in multiple places, which should be more user-friendly. A centralized interface for managing these configurations is desired.
For how long have I used the solution?
I have been working with Amazon SageMaker for nearly three years.
What do I think about the stability of the solution?
Amazon SageMaker's stability depends on how well-configured the entire setup is. Due to the interconnected dependencies within the system, the learning curve may be steep for new users. However, with proper configuration, the overall stability is adequate.
What do I think about the scalability of the solution?
Amazon SageMaker offers a high level of scalability. It allows dynamic resource allocation and supports large datasets through various features like multi-model endpoints and flexible instance configuration, scaling up or down according to requirements.
How are customer service and support?
Technical support for Amazon SageMaker involves communication through web chats or telephone based on the support agreement.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
In AWS, we used to build the whole pipeline model by ourselves, component by component. With Amazon SageMaker, costs have been optimized as it includes pre-configured components that reduce overall expenses.
How was the initial setup?
The initial setup of Amazon SageMaker can be achieved quickly if the default configuration is used. However, setting it up more customizable, such as for specific requirements, can make the process time-consuming, earning an eight out of ten in terms of ease.
What about the implementation team?
A single knowledgeable person with expertise in ML and cloud can handle the deployment and maintenance of Amazon SageMaker.
What was our ROI?
We have seen a significant reduction in costs using Amazon SageMaker. Building any ML lifecycle benefits from SageMaker's pre-configured components, which bring down the overall cost compared to setting up all components separately.
What's my experience with pricing, setup cost, and licensing?
While Amazon SageMaker is expensive compared to other cloud vendors, certain cost optimizations can be made with proper setup and configuration knowledge. Greater visibility from AWS regarding cost-impacting configurations would be beneficial.
Which other solutions did I evaluate?
No other solutions were evaluated outside of AWS, as we were setting everything up within AWS before opting to use Amazon SageMaker.
What other advice do I have?
I rate SageMaker eight out of ten.
New users should conduct a pilot or proof of concept with Amazon SageMaker to see if it aligns with their business use cases. Evaluate and understand the integration with other AWS services and ensure the team has adequate knowledge to handle monitoring, model performance, and managing costs efficiently. Engaging with the community to remain updated on any misconfigurations is also advisable.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
*Disclosure: I am a real user, and this review is based on my own experience and opinions.