SageMaker : Good option for 'fail early learn fast' in ML and DLhttps://www.trustradius.com/machine-learningAmazon SageMakerUnspecified8141012019-06-03T00:15:45.686Z
Updated June 14, 2019
SageMaker : Good option for 'fail early learn fast' in ML and DL
Score 9 out of 101
Overall Satisfaction with Amazon SageMaker
We are using the SageMaker service from AWS for POC, and to build the final model on the large dataset of healthcare domain under the R&D department. SageMaker also provides hosting functionality, so that we can host a created model for the end-level application which is accessible through a simple API call from any application.
- Provided an instance of Jupyter notebook for development script, which made it very easy to manage and develop any script.
- Our system is cloud-based, and we are charged only for what we use and how long we use it.
- We can choose multiple servers for Training, without any headache of distribution.
- Most of the libraries are supported.
- All training, testing, and models are stored on S3, so it's very easy to access whenever we require it.
- It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
- Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.
- Using SageMaker, we can truly implement 'fail early, learn fast,' using an on-demand server for training.
- It also saves your money from investing in a physical server for very rare use.
- However, the pricing is high, but it will cost you only for what you use.
Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
Well suited scenarios:
- For quick POC of ML and DL.
- To train a model on a large dataset using multiple servers.
- To host a model to be used by multiple applications.
- For data analysis tasks.
- For a data scientist who has less of a programming background.